methodology of air pollution project

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A Methodology of Assessment of Air Pollution Health Impact Based on Structural Longitudinal Modeling of Hierarchical Systems and Fuzzy Algorithm: Application to Study of Children Respiratory Functions

Friger, M; Bolotin, A; Peleg, R

Ben-Gurion University Of The Negev, Beersheba.

Introduction:

Assessing the effects of air-pollution is a significant problem in the field of modern environmental epidemiology. When modeling these effects it is important that the models must be epidemiologically meaningful and robust (that is, insensitive to variations in the model parameters). The objective of this paper is to propose a methodology for the assessment of the health impact of air pollution. The proposed methodology involves the construction of models for complex dynamic hierarchical systems in environmental epidemiology and their problem-oriented interpretation.

The principal stages of the proposed methodology are:

• Creation of a multivariate hierarchical structural model based on system analysis.
• Generation of a mathematical formalization for this model.
• Development of a statistical model for a particular study case based on the mathematical formalization, using the generalized estimating equations technique and time-series analysis. At this stage, for a dichotomized dependent variable, a special fuzzy algorithm was used. The algorithm employed fuzzy membership functions instead of the binary variable to obtain robust regression models.
• Use of the “multi-layered” approach for model interpretation developed by the authors. This approach involved the creation of special functional time-dependent coefficients that reflect the effect of air pollutants at a given time. These coefficients allow an epidemiological meaningful model interpretation. Thus, they can be used for air-pollution health effects assessment.

The proposed methodology was used to analyze data collected from lung function measurements in 165 children from February-September 2002 (about 4000 individual daily records). The subject variables were age, gender, body-mass index, and place of residency. The meteorological variables included daily maximum temperature, average humidity and barometric pressure. The air-pollutant variables were suspended particles, ozone, nitrogen and sulphur oxides. In addition, the effects were studied up to a 3-day lag. The results demonstrated a statistically significant effect of air-pollution on lung function. Changes in most of the pollutants did not cause a critical decrease in lung function. However, for the observed period, a 10 mkg/m 3 increase in ozone was associated with a mean decrease in lung function of 6 units for a one-day delay.

Discussion and Conclusions:

The assessment of the health effects of air pollution and their interpretation make epidemiological sense, lending support to the correctness of the proposed methodology. Testing the models by changing the dichotomization cutoff for the lung function variability shows that the models based on the proposed fuzzy algorithm are robust.

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California Air Resources Board

Carb’s methodology for estimating the health effects of air pollution.

Research Division Email [email protected] Phone (916) 445-0753

CARB estimates premature death and other health effects related to PM2.5 exposure based on a peer-reviewed methodology developed by the U.S. Environmental Protection Agency (U.S. EPA, 2010).  The methodology is used to estimate the reduction in premature deaths and other health effects associated with emission reductions of PM2.5 emitted directly from sources and secondary PM2.5 formed in the atmosphere from chemical precursors.

CARB periodically updates the underlying air quality and demographic data to make sure it reflects current conditions. The most recent update was May, 2019.

A brief summary and a more detailed description of CARB’s methodology are available.  The basis for CARB’s methodology is described in Appendix J of the Truck and Bus Initial Statement of Reasons which was presented to the Board in 2010.  More information on the way the US EPA estimates health impacts of regulations can be found at the links below.

The following links provide more background information on the methodology for estimating health effects from PM2.5 exposure.

Methodology

• Summary - This page is an overview of the methodology for estimating health impacts.
• Detailed description - This page provides a detailed description of the methodology for estimating health impacts.
• Data - Contact CARB Research (Email: [email protected] ; Phone: (916) 445-0753) to obtain data used in the calculations.

CARB documents

• Truck and Bus Initial Statement Of Reasons Appendix J – This is a description of the methodology using 2006-2008 air quality data.

Documents related to U.S. EPA’s methodology

• Benefits Mapping and Analysis Program (BenMAP)  – BenMAP is U.S. EPA’s software for estimating health effects associated with changes in air pollution levels.
• Technical Support Document: Estimating PM2.5- and Ozone-Attributable Health Benefits   – U.S.  EPA's approach to quantifying the number and value of health impacts attributable to changes in ambient PM2.5 and O3 exposure.
• Integrated Science Assessment for Particulate Matter – U.S. EPA’s assessment of relevant PM health studies that informs the review of the national ambient air quality standards for PM.

November 2022 Update – New and Updated Health Endpoints 

In November 2022, CARB staff released a bulletin providing a list of new and updated PM2.5 health endpoints to be used in CARB’s health analyses for plans and regulations, along with the scientific basis for choosing those endpoints and information on the valuation. These endpoints will be used in CARB health analyses for plans and regulations starting in early 2023. 

The new endpoints are as follows: 

• Cardiovascular emergency department visits
• Acute myocardial infarction (nonfatal) 
• Asthma onset 
• Asthma symptoms/exacerbation 
• Work loss days 
• Lung cancer incidence 
• Alzheimer’s disease 
• Parkinson’s disease 

The following endpoints have been a part of CARB’s routine health analysis methodology since 2010, but the underlying studies used for the effect estimate for these endpoints have been updated to more recent studies: 

• Cardiovascular hospital admissions 
• Respiratory hospital admissions 
• Respiratory emergency department visits (previously, CARB had specifically analyzed asthma but are updating this endpoint to include all respiratory diseases) 

This inclusion of new and updated endpoints is part of a larger project to expand CARB’s health analysis methodology in response to new research over the last decade. The project began in April 2020 when the Board directed staff, through Board Resolution 20-13 (Health Evaluation of Air Quality and Climate Regulations and Programs), to expand our health analysis approaches (e.g., updating the health endpoints) in order to capture a broader and more comprehensive range of benefits from reductions in air pollution.

• Board Resolution 20-13 (April 2020), “Health Evaluation of Air Quality and Climate Regulations and Programs” 
• Public workshop slides and recording (December 2021), “Evaluating New Health Endpoints for Use in CARB’s Health Analyses” 
• Updated Health Endpoints Bulletin (November 2022) 

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Atmospheric modeling is used by air quality managers to make decisions on effective and efficient ways to implement the National Ambient Air Quality Standards (NAAQS) and improve air quality. EPA has an extensive air quality modeling program that develops, evaluates, and applies models to support a wide variety of air quality management needs.

Advances in modeling enables users to better estimate the relationship between sources of pollution and their effects on ambient air quality ,  predict the impacts from potential emission sources , and simulate ambient pollution concentrations under different policy scenarios. They are critical for determining the relative contributions from different sources, monitoring compliance of air quality regulations, and making policy decisions.

This research is also enhancing the ability to conduct multipollutant air quality assessments at local, regional, national, and global scales in addition to developing multimedia and multi-stressor models to address complex environmental issues. The research objectives are:

• Characterize the role of background air pollution on NAAQS attainment and implementation
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Modeling sars-cov-2 aerosols and evaluating ways to inactivate aerosolized sars-cov-2, related links.

The Community Multiscale Air Quality (CMAQ) Modeling System is EPA’s premier modeling system for studying air pollution from local to hemispheric scales. For more than two decades, EPA and states have used CMAQ to translate fundamental atmospheric science principles to policy scenarios to support air quality management decisions. CMAQ is continually updated to incorporate knowledge on the state-of-the science and harness high performance computing power to more effectively and efficiently characterize air quality and protect human health and the environment.

CMAQ combines meteorological, emissions, and air chemistry transport and deposition models to explore the estimated short- and long-term impacts of different policy and regulatory options, including actions to attain the NAAQS, and long-term impacts of the changing environment. Developed and maintained by EPA scientists, the CMAQ modeling system continues to evolve to better represent how complex mixtures of air pollutants are formed, transported, and eventually removed from the atmosphere.

Researchers lead efforts to conduct and apply fundamental physical science that improves CMAQ’s representation of complex atmospheric chemistry and dynamics pertinent to emerging problems and contaminants. A global user community has fostered collaborations with state, federal, industrial, and academic institutions in the United States and around the world to assess and improve the model’s functionality.

Currently, CMAQ developers are broadening the model’s scope to enhance its ability to consider atmospheric phenomena from the global scale to the neighborhood scale. These efforts are important for understanding the impacts of human activities and intervention strategies at all levels . , including the impacts of agricultural sources such as animal feeding operations (AFOs).

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• Celebrating 25 Years of Air Quality Modeling Excellence   (Science Matters Newsletter)
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Air quality dispersion models predict the impact of pollutants released from various sources such as power plants and roadways. The models are used by EPA, states, tribes and local agencies to assess control strategies, regulate emissions, and evaluate mitigation options, particularly during the planning and permitting phases of projects. 

EPA researchers, in collaboration with other scientists, developed the Air Quality Dispersion Model (AERMOD), which is the Agency-preferred and recommended dispersion modeling system used today. AERMOD is used to model the impact on air quality from sources that emit a variety of pollutants regulated by the EPA, including carbon monoxide, lead, sulfur dioxide, nitrogen dioxide and primary particulate matter; and hazardous air pollutants, also known as air toxics. 

Research continues to provide updates to the model. To improve modeling capability, there is a need for more information on the influence that buildings, roadways and other structures have on the flow and dispersion of air pollution. Researchers are leading efforts to develop data sets and algorithms to improve air dispersion modeling simulations to better capture these near-source effects.

Studies are conducted in the meteorological wind tunnel at the Fluid Modeling Facility in Research Triangle Park, North Carolina, and in the field. The tunnel is large enough to simulate pollution dispersion of a scaled replica of a building, power plant or other object of interest and surrounding topography. The tunnel has a large test section measuring 3.65 meters across and 2.1 meters tall and provides an extensive downwind distance over which to study the development of a plume emitted from a source.

Current projects include studies of the potential of noise barriers for pollution mitigation in the near-road environment , and studies of the role of urban structures on flow and dispersion.

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Estimation of emissions, fate and transport of indoor air pollutants is an essential part of multi-pathway exposure assessment since most people spend a large portion of their time indoors. Indoor air modeling plays an important role in indoor air research because laboratory and field testing is costly, time-consuming and technically challenging to appropriately characterize chemicals in a broad range of indoor environments.

Researchers at EPA have developed indoor air modeling programs to assist with understanding indoor air pollution. They include:

• Simulation Tool Kit for Indoor Air Quality and Inhalation Exposure (IAQX)
• Indoor Semi-volatile Organic Compounds (i-SVOC)
• Parameters (PARAMS) Program Version 1.1 for indoor emission source modeling

These programs are Microsoft Windows-based and user friendly. PARAMS implements 30 methods for estimating the parameters in indoor emissions source models, which are an essential component of indoor air quality and exposure models. IAQX and i-SVOC are used for dynamic modeling of the emissions, transport, and absorption of pollutants in the indoor environment. They can be used as a front-end component for stochastic exposure models and provide estimates of the pollutant’s distributions in indoor media in the absence of experimental measurements.

IAQX consists of five stand-alone simulation programs. A general-purpose simulation program performs multi-zone, multipollutant simulations and allows gas-phase chemical reactions. The other four programs implement fundamentally based models for special purposes. In addition to performing conventional indoor air quality simulations, which compute the time concentration profile and inhalation exposure, IAQX can estimate the adequate ventilation rate when certain air quality criteria are provided by the user, a unique feature useful for product stewardship and risk management.

The i-SVOC program estimates the emissions, transport, and absorption of semi-volatile organic compounds (SVOCs) in the indoor environment as functions of time when a series of initial conditions are given. The program covers six types of indoor compartments: air (gas phase), air (particle phase), sources, sinks (i.e., absorption by interior surfaces), contaminant barriers, and settled dust. The key input parameters of the program include the solid-air partition coefficients, solid-phase diffusion coefficients, and gas-phase mass transfer coefficients.

Using these indoor air modeling programs, scientists can gain a deeper understanding of the hazards and risks of many chemicals. The results will support EPA, states, tribes and local agencies with refining existing risk assessments and making policy decisions to minimize exposure and protect human health and the environment from thousands of existing and emerging chemicals indoors.

Chemical Mechanisms to Address New Challenges in Air Quality Modeling 

In 2020, EPA awarded nine Science to Achieve Results (STAR) grants to conduct research to improve air quality models by providing a better representation of atmospheric chemical reactions, which is known as chemical mechanisms. These chemical reactions are relevant to the chemical transformation of air pollutants, such as ozone, particulate matter (PM) and air toxics, which can cause adverse human health and environmental effects.

New insights on atmospheric chemistry and advancement in chemical mechanisms will improve air quality model predictions, which may inform the development of more effective strategies for improving air quality. The project period for the grants is August 1, 2020 to July 31, 2023.

Many chemical mechanisms currently used in multi-pollutant air quality models were originally developed when air quality management and scientific studies focused on extreme pollution episodes in urban areas. Since the early 1990s, pollutant emissions from many man-made sources have declined substantially, and the National Ambient Air Quality Standards (NAAQS) have been made more protective. Today, studies show some emission sources previously considered insignificant, require further scientific evaluation, and new chemical compounds are emerging as important contributors to air quality concerns.

Additional chemical mechanism research is needed to understand these and other air quality issues.

The research objectives for these grants are: 

• Development of state-of-the-science chemical mechanisms that can be easily updated to reflect evolving scientific understanding
• Development and evaluation of algorithms, numerical techniques and software tools to simplify detailed chemical mechanisms into application-specific condensed (i.e., simplified) mechanisms appropriate for use in air quality models
• Applying newly developed chemical mechanisms in air quality models to investigate air quality issues relevant to the United States
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The natural world that consists of our atmosphere, land, water, and ecosystems is interconnected in many ways. Because of this connectivity, when a contaminant or pollutant is introduced into the environment, there can be a cascade of multiple impacts.  

Many of the nation’s environmental problems require an understanding of how contaminants move  across air, land and water and how they may transform. For example, deposition of gases and particles from the atmosphere to the water and land surfaces results in acidification and eutrophication, which, in turn, impacts the abundance and diversity of aquatic species. In another related case, excessive nitrogen (N) and phosphorus (P) concentrations in surface water, resulting from flooding, runoff or atmospheric deposition, contribute to harmful algal blooms. This can be accelerated by increased water temperatures and lead to large oxygen depleted zones or hypoxia and the loss of marine life and biodiversity.  

EPA scientists work to understand the interconnectedness of the environment across media by collecting and evaluating observations (e.g. pollutant concentrations in various media, dissolved oxygen levels, etc.) and developing, evaluating and applying multi-media modeling tools. The modeling enables simulation of the transport and transformation of chemicals through different media (e.g. air, water, soil) to provide data that can be used to address environmental challenges such as acid rain, nitrogen deposition and other environmental problems. One research priority is to characterize the nitrogen cycle, which requires knowledge of many atmospheric, soil and plant processes. 

Multi-media models are used to characterize nitrogen loading to watersheds such as the Chesapeake Bay and Albemarle-Pamlico estuaries and the Nooksack River Basin and are also used to evaluate nitrogen exceedances for terrestrial ecosystems.

Scientists are developing a coupled modeling system that includes exchange processes between existing air quality, meteorology, hydrology, and water temperature models to identify hotspots of high-water temperatures areas, which are correlated to high-nutrient loads in the Mississippi River Basin. They are also developing a high temporal (daily) and spatial (30-100 meters) distributed modeling system to simulate nitrogen yields from agricultural land use, as well as atmospheric deposition loads.

In another project using meteorological and hydrology modeling, scientists are exploring methods of projecting the risks to ecosystems and stormwater management from changes in extreme rainfall. These and other studies on multi-media modeling and measurements are improving the ability to simulate the potential impacts of pollutants across time and space.

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8 Student Experiments to Measure Air Quality

As pollution is becoming more rampant worldwide, educators are deeply encouraged to teach students about the science of pollution and what they can do to care for the environment. From the release of greenhouse gases, worsening climate change, and burning of fossil fuels, the rate at which pollutants are produced is unprecedented. Hence, we have suggested 8 experimental ideas that teachers can use to actively involve their students to measure air quality. These ideas range from qualitative to quantitative methods and promote active discussion on which type of method would prove the most effective in producing reliable data. By measuring air quality, students can make sense of the different standards of air quality and recognize when it becomes appropriate to mitigate their effects. Without further ado, let’s jump into it!

PM 2.5 Meter

Particulate Matter (PM) 2.5 refers to particles or compounds which spans less than 2.5 micrometers in diameter. They are fine particles that are commonly produced from factories, vehicle exhaust, burning of crops or volcanic eruptions. Long-term exposure to high concentrations of these particles can put people at risk of developing respiratory illnesses, inflammation, trigger asthma and can even lead to lung cancer.

There are many types of PM 2.5 meters in the market, with some of them capable of measuring other types of pollutants as well. PM 2.5 is most commonly measured in units of µg/m3 (micrograms per meter cubed), or through AQI measurements (meter below ).

Depending on the meter you purchase, it may display either one of the units of measurement. However, keep in mind that the two measurements are not directly proportional to each other as measurements in µg/m3 are representative of the actual amount of PM in the area whereas AQI measurements are subjective to the air quality standards that have been internationally agreed upon for your particular region. This difference in proportionality is represented in the following graph :

Having a corresponding AQI level alongside PM2.5 measurements can help students measure the precise amount of PM2.5 in a certain area and use them to determine the quality of the air. Students could measure air quality in different areas, comparing the results obtained and discuss the probable causes and sources of PM from the location. It is also a great way to measure quantitative values that are useful for writing scientific reports while reinforcing the numbers with a qualitative interpretation of the air quality.

You could perform these measurements with your students for a variety of experiments. For example, by comparing the PM measurements from outdoor areas to indoors, students would become aware of the differences in air quality and discuss the possible outdoor/indoor pollutants that may cause one to have higher PM levels than the other. Another great experiment would be to compare the PM levels of rooms that use purifiers and those that do not. This would demonstrate the effectiveness of air purifiers to students and how they are beneficial to maintaining their general wellbeing.

The following graph compares the different levels of PM 2.5 and US AQI PM 2.5 measurements provided by sarta.innovations2019.org :

Blue Sky Test

The color of the sky is a reliable qualitative method to measure air quality. The color can change due to airborne particles that reflect and refract light. For example, a blue sky would indicate little to no air pollution whereas bright red ones are a result of heavy pollution.

The University of Southern California developed an algorithm through its mobile sensing project to measure the air quality by analyzing pictures of the sky taken from the Android app that they created. The pictures that would be taken would take into account the user’s location, orientation, time taken and transfer the data collected into their server. It would calibrate the image and compare it with their own model of the sky.

Although their app isn’t officially listed in the Play Store, all you have to do is click this link from your Android mobile device and click ‘Download Android App’ on from their website. If your phone is preventing the download, click here to find out more about installing APK files on your phone.

Mountain Visibility Test

Similar to the blue sky test, checking the visibility of mountains, or a large construction that can be seen from a long distance is also a qualitative indicator for air pollution. When we see places that are more polluted, we easily recognize the thick haze and dust that clearly obscures the view. But if we live in a polluted area, a clear view of mountains or other constructions may seem foreign in comparison as demonstrated from the following pictures provided by the US National Park Service :

This method measures visibility as an indicator of air pollution. A great idea is to get in touch with schools from different areas to see if they would like to collaborate to gather picture samples of their view. This way, teachers could show their students what mountains or distant constructions from various places look like. This can prompt a discussion about why some areas are more visible than others while explaining how air pollution impacts the view.

Students could also compare the images with public available AQI data from the region and see if there is a direct correlation between the AQI and visibility. Using the previous AQI Index table, students would be able to understand the different standards of air quality and associate it to qualitative observations on their surrounding environment. Furthermore, you could perform this experiment using AQI websites such as AirVisual or aqicn.org to identify the AQI values of different locations worldwide and compare it with images of landmarks in a particular country from which students could effectively assess the visibility.

This is a simple and easy qualitative analysis that can be performed anywhere in the world. However, it is advisable to make observations in the morning when there is the least fog and other factors impacting visibility .

Sticky Tape Method

Although the most dangerous particles are smaller in size, it is still a good indicator of air pollution to also measure the amount of larger particles such as dust, soot, dirt, smoke that can be potentially seen.

The sticky tape method is very simple, all you have to do is cut a small piece of transparent sticky tape and attach it to the bark of a tree or the surface of a building. Leave it for 10 seconds to let any PM on the surface stick onto the tape, peel the tape off and stick it onto a piece of paper. Students should be advised to label the time and location at which they took the sample.

Students could perform experiments by either collecting tape samples in the same location over different periods of time or taking samples in different locations at a certain period of time depending on their chosen independent and dependent variables. They can make qualitative observations of how PM levels change in different times and locations. This can be expanded by discussing the possible reasons as to why some areas or times have more PM in the air than others.

Lichen Observation & App

Sulfur dioxide (SO2) is a gas with a pungent scent which is known to be harmful towards our health. It is mostly generated from the burning of fossil fuels from industrial processes such as the generation of electricity from burning coal. It reacts to evaporated moisture in the air to produce several acidic compounds such as sulfuric acid, which can cause acid rain when dissolved in rainwater, leading to the acidification of forests.

Nitrogen can also be an overlooked pollutant as it is a common constituent in fertilizers and organic waste from households and sewage. When they have washed away into water bodies, it increases the acidity of the water, causing numerous wildlife deaths and disrupting the ecosystem. Like sulfur dioxide, it also causes acid rain when neutral nitrogen particles react with lightning in the air and mix with rainwater.

Lichen is an effective bio-indicator of sulfur and nitrogen pollutants. If lichen is a naturally occurring substance in your area, it will not be present if they are in the air and there would be green algae in its place. Many more species can act as a bioindicator for particular pollutants depending on vegetation that are sensitive or tolerant to them. A massive study was conducted using lichens to measure the air quality throughout the UK by the OPAL Air Survey .

The study conducted modeled the relationship between lichens as a bioindicator, nitrogenous pollutants, and their climate. Furthermore, the data was easily collected by everyday citizens throughout the UK and can be performed as school experiments as well. The map of the UK on the left demonstrated the amount of nitrogen dioxide (NO2) around the country while the one on the right referred to NHx radicals such as ammonia (NH3) and ammonium (NH4), which can cause ammonia pollution. The following image is their result:

The UK Centre for Ecology & Hydrology developed the Lichen Web-App , which provides guidelines on how to identify what type of lichen is suitable for testing, how to perform chemical tests on them and a comprehensive list of different species that are sensitive or tolerant towards nitrogen. It also enables you to track and record any trunks and branches that have lichens on them. They also created a measurement system called Nitrogen Air Quality Index (NAQI) to accurately associate the different levels of nitrogen to indicate their corresponding level of air quality.

Students could emulate this study on a much smaller scale and explore their environment for lichen or other similar species. This would also make them aware of how vegetation is often sensitive towards pollution.

Palmes Passive Diffusion Tubes

Nitrogen can exist in many forms, one of them being nitrogen dioxide (NO2). Nitrogen dioxide is a gaseous pollutant produced from the burning of fossil fuels such as those in power plants and vehicle exhausts. It undergoes a process in which neutral nitrogen (N2) and oxygen (O2) particles react in high temperatures to produce nitrous oxides (NOx) including NO2, all of which can inflict respiratory conditions such as inflammation, coughing, irritations, etc. This is clearly demonstrated from the image on the right which was performed in an experiment from the University of Edinburgh.

Passive diffusion tubes are an effective long-term method to measure nitrogen dioxide. These small plastic tubes contain a mesh disc made of steel covered with a chemical called triethanolamine (TEA). If nitrogen dioxide is present and passes through the mesh, it would react with TEA and change the color and chemical composition. Diffusion tubes can measure the change in nitrogen dioxide levels over many months inside classrooms or outside your school based on how much TEA is left in the tube.

Ozone Testing Experiments

Ozone (O3) is a gas that is popularly known as a gaseous layer in the stratosphere which protects the earth from harmful UV radiation from the sun. However, ones at the troposphere are mainly the result of the chemical reactions between nitrous oxides (NOx), volatile organic compounds (VOCs) and the sunlight. At high concentrations, they can cause chest pains, coughs, throat irritations and are especially harmful to those suffering from respiratory conditions such as asthma.

We can test for the presence of ozone in two different ways:

Ozone badges are very simple and can be made into different forms. All of them rely on a change in color when high concentrations of ozone are present. The badges as seen from the image are commercially produced indicators that are commonly used by workers who are required to operate in areas with elevated ozone concentrations.

For a more advanced chemical experiment, you could also perform the Schoenbein experiment. Students would require cornstarch and potassium iodide to make indicator strips that would react with ozone if present in the air, evidently turning blue or purple. According to the resulting Schoenbein number from the color scale below, we can determine the amount of ozone present in parts per billion (ppb) as seen from the following from the graph.

It is important to perform this experiment in days with low humidity (the lines from the graph represent how the Schoenbein numbers vary based on the different percentages of humidity in the air). Under these circumstances, ozone would be more likely to break apart into atmospheric oxygen. This experiment also yields the best results in the ozone season, which occurs during heated temperatures throughout the summer and in areas with high vehicle activity.

While this method is relatively safe, it is advised to perform this under the supervision of Chemistry teachers who can provide them with the chemicals and laboratory equipment needed.

Surface Wipes

Surface wipes are similar to the sticky tape method, which simply involves wiping a cotton bud on a surface to observe how much PM was released in a particular time or area. Students can compare the cotton buds that were wiped on the surfaces that are more exposed to the ones less exposed to pollutants, such as on the opposite sides of a handrail or bench. The following video is a lighthearted and entertaining experiment performed by a YouTuber from Sydney to observe the city’s air quality, which has dramatically worsened as of late due to the Australian bushfires:

Teachers and students are encouraged to be creative, improvise and innovate experiments similar to this. That way, educators could create a stimulating and critical learning environment for students to teach them about scientific research methods.

As a teacher or parent, you can choose from a myriad of creative options to teach your child how to measure air quality. Depending on their style of learning and personal preference, you can weigh the benefits of performing qualitative or quantitative methods to help them understand the state of the environment. By performing diverse experiments, they would be able to understand how different collection methods result in corresponding data types. After experimenting with multiple methods, they can then determine which type would be the most suitable to fulfill the research’s purpose. We hope that these experiments would be able to pique their curiosity and encourage them to make meaningful discussions about the health effects and environmental impacts of air pollution!

by Carisa A. Feb 16, 2020

Open and Accurate Air Quality Monitors

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Assessment of NO2 and PM2.5 exposure with air quality sensors

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• Heimann, Frank
• Vogt, Ulrich

According to the World Health Organization, poor air quality contributes heavily to the Global Burden of Disease, causing more than 6.7 million deaths each year due to both ambient air pollution and household air pollution. With advances in air pollution monitoring technology, evidence on the adverse health effects of air pollution has been increasing. Still, the understanding of personal exposure is limited by the low spatial resolution of fixed outdoor monitoring stations. Low-cost sensors have the potential to enhance personal exposure prediction at scales required for population-based research.In this study, we carried out a pilot project to evaluate the feasibility of using low-cost sensors at fixed-locations for epidemiological investigations. Stationary sensor systems for NO2 and PM2.5 were custom-built and deployed both in- and outside the homes of individuals diagnosed with asthma or chronic obstructive pulmonary disease (COPD). Measurements were taken for approximately 30 days at each participant's home. The study was designed to evaluate the performance of the air quality sensors over a longer timeframe, which so far has not been thoroughly studied (Sesé et al. 2023). Participants self-reported symptom data to study the relationship between indoor air quality and health. Participants recorded their daily activities as well, as part of examining the exposure estimates and indoor pollutant sources. To evaluate the exposure misclassification, the potential dose was calculated using the data of an outdoor monitoring station and the indoor sensors, as well as the generic and the activity-specific inhalation rate. Steps completed prior to this analysis include a study on a low-cost dryer for the PM sensor to prevent the overestimation of the mass concentration due to the hygroscopic growth of particles (Chacón-Mateos et al. 2022), and processing of the NO2 data using machine learning to evaluate the uncertainty, reproducibility, reliability, and sensitivity of the sensors. The results of this work highlight the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. With the appropriate methodology and a robust calibration, air quality sensors can provide us with useful information and show promise for epidemiological investigations.References:Sesé, L.; Gille, T.; Pau, G.; Dessimond, B.; Uzunhan, Y.; Bouvry, D. et al. (2023): Low-cost air quality portable sensors and their potential use in respiratory health. In Int. J. Tuberc. Lung Dis. 27 (11), pp. 803-809. DOI: 10.5588/ijtld.23.0197.Chacón-Mateos, Miriam; Laquai, Bernd; Vogt, Ulrich; Stubenrauch, Cosima (2022): Evaluation of a low-cost dryer for a low-cost optical particle counter. In Atmos. Meas. Tech. 15 (24), pp. 7395-7410. DOI: 10.5194/amt-15-7395-2022

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Low-cost Techniques to Measure and Predict Air Pollution Exposure

 Exposure to air pollution is the fifth leading cause of death worldwide. This risk is dominated by exposure to fine particulate matter (PM 2.5 ) and is deemed even more critical in low- and middle-income countries due to a lack of data available to build effective policies. The scarcity of actionable air quality data in these regions is due to thehigh cost of research-grade air pollutant monitors. The core objectives of my thesis are to (1) develop a low-cost method to measure atmospheric black carbon, a major contributor to PM 2.5 in developing nations, at existing PM 2.5 monitoring sites, (2) use the method to measure the BC component of PM2.5 to identify leading combustion sources contributing to pollutant concentrations in African cities, and (3) develop a framework for developing a robust deep learning-based generalized model to predict PM 2.5 pollutant concentrations that is transferable to other locations. 

US Department of State collects air pollutant data at selected US embassies and consulates around the world to inform embassy personnel and citizens about the local air quality. These measurements are made by Beta Attenuation Monitors (BAMs) to measure hourly ambient PM 2.5 concentrations. These BAMs at the embassies collect PM 2.5 onto glass-fiber filter tapes at a circular spot with a fixed flow rate of 1m 3 /h -1 for approximately an hour and pass beta ray through the particle deposit. Hourly PM 2.5 concentration is calculated with Beer-Lambert law by utilizing the attenuation of beta rays through the spot. The BAM moves the spot to sample on a clean area of the filter tape and continues this process until the tape is exhausted. A new tape is installed and the used tapes are usually discarded after a brief period of storage. Chapter 3 of this thesis investigates a cost-effective method to leverage used tapes from these existing BAMs to measure ambient black carbon concentration at an hourly resolution. We developed an image reflectance-based method to determine hourly black carbon (BC) concentrations from red light reflectance using cell phone cameras. The method relies on the working principle that the BC loading on a filter sample is correlated to intensity of reflected red light from the sample. The hourly effective detection limit of the method is estimated to be around 0.15 μg/m - 3 of BC, which makes it suitable to use in most micro-environments, especially the ones with high combustion emissions. This method only requires a reference card sheet and a cellphone camera. Both of these items are easily accessible making this method practically a "zero" cost measurement technique. 

We used this method to measure hourly BC concentrations at multiple locations in Africa and compared it to our measurements in Pittsburgh. Unregulated emission sources are one of the major factors for the high pollution levels in developing nations. For example, primary sources of air pollution in Africa are unregulated transport emissions, burning of solid fuels for cooking and heating in winter seasons, open burning of crop residues and burning fossil fuel for electricity production. Air quality measurements can provide evidence on these high emitting sources for effective policymaking, further alleviating the air pollution scenario in these regions. Chapter 4 presents measurements on BC component of hourly PM 2.5 data with our image reflectance-based method applied on used BAM tapes from multiple cities in Africa, which will be used in investigating and identifying primary sources of pollutants at these locations. The cities in this study include Abidjan (Côte d’Ivoire), Accra (Ghana) and Addis Ababa (Ethiopia). The measurements show a significantly high BC levels across the cities and seasons in Africa. Availability of hourly BC information, in addition to PM 2.5 measurements, reveal high contribution of combustion emissions in the local air pollution. In Addis Ababa, BC composed 20% of PM 2.5 both in summer and winter seasons at two locations within the city. BC measurements in Abidjan showed a higher BC level during wet season due to increased burning of solid fuels to heat the houses as this period experiences lower temperatures. Hourly data also allowed us to derive diurnal patterns of BC for all cities. Accra showed a unique peak at 2 am in the night. This peak was later identified to be from diesel generators used by US embassy at Accra to meet their power demands during power outage incidents and/or illicit waste burning at night. Thus, our measurements reveal tons of information on pollutant levels as well as emission sources. 

A step further in informed policymaking for reducing exposure during hazardous levels of air pollution is to develop a forecast model that can predict air pollutant levels with the use of temporally dynamic variables. A major benefit of these forecast models is their ability to predict high pollution episodes so that both precautionary and preventive measure can be taken in advance to alleviate the exposure levels. Precautionary measures include wearing masks or staying home in case of severe pollution episodes, whereas preventive measures encompass coordinating with local industries and commercial plants to dampen their activities to reduce their emissions. 

Studies have developed variants of land-use regression-based models to forecast air pollution, but these models show poor prediction capabilities and their performance saturate despite inclusion of myriad features for training models. Most of the high pollution episodes overlap with period of temperature inversions, which is period of a very low planetary boundary layer height with minimal atmospheric circulation trapping air pollutants close to the ground. Therefore, we built models that use boundary layer height and related meteorological parameters among training features. Chapter 5 investigates deep learning models to build a novel neural network-based forecast model that utilizes training covariates, including air pollutant and weather-based parameters forecast from GEOS-CF model to generate a more robust and generalized empirical prediction model to forecast PM 2.5 a day in advance. We use 5 years of low-cost sensor-based PM 2.5 data as the ground truth in training the models to predict PM 2.5 for locations in Monongahela Valley, Pennsylvania. 

Degree Type

• Dissertation
• Mechanical Engineering

Degree Name

• Doctor of Philosophy (PhD)

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• Published: 05 July 2024

Recent PM 2.5 air quality improvements in India benefited from meteorological variation

• Yuanyu Xie   ORCID: orcid.org/0000-0001-5966-0482 1 ,
• Mi Zhou   ORCID: orcid.org/0000-0001-8600-1503 1 ,
• Kieran M. R. Hunt 2 , 3 &
• Denise L. Mauzerall   ORCID: orcid.org/0000-0003-3479-1798 1 , 4  

Nature Sustainability ( 2024 ) Cite this article

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• Atmospheric chemistry
• Environmental monitoring

Improving air quality amid rapid industrialization and population growth is a huge challenge for India. To tackle this challenge, the Indian government implemented the National Clean Air Programme (NCAP) to reduce ambient concentrations of particulate matter with diameters less than 2.5 μm (PM 2.5 ) and 10 μm (PM 10 ) in hundreds of non-attainment cities that failed to meet the national ambient air quality standards. Here we evaluate the efficacy of the NCAP using data from the national air quality monitoring network combined with regional model simulations. Our results show an 8.8% yr −1 decrease in annual PM 2.5 pollution in the six non-attainment cities with continuous air pollution monitoring since 2017. Four of these six cities achieved over 20% reductions in PM 2.5 pollution by 2022 relative to 2017, thereby meeting the NCAP target. However, we find that ∼ 30% of the annual PM 2.5 air quality improvements, and approximately half of the reductions during the heavily polluted winter months, can be attributed to favourable meteorological conditions that are unlikely to persist as the climate warms. Meanwhile, in 2022, annual PM 2.5 levels in 44 out of 57 non-attainment cities with continuous monitors still failed to meet air quality standards. This work highlights the need for substantial additional mitigation measures beyond current NCAP policies to improve air quality in India.

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Data availability.

Surface PM 2.5 and other air pollution data from the CAAQM network are available at https://airquality.cpcb.gov.in/ccr/#/caaqm-dashboard-all/caaqm-landing . Surface PM 2.5 data from the US AirNow network are available at https://www.airnow.gov/international/us-embassies-and-consulates/ . Manual monitoring data for PM 2.5 and other air pollution data are available at https://cpcb.nic.in/manual-monitoring/ . The CEDS emission database is available via GitHub at https://github.com/JGCRI/CEDS/ . The EDGAR emission database is available at https://edgar.jrc.ec.europa.eu/dataset_ap61 . The ECLIPSE emission database is available at https://iiasa.ac.at/models-tools-data/global-emission-fields-of-air-pollutants-and-ghgs . Satellite observations of SO 2 and NO 2 concentrations from OMI are available at https://giovanni.gsfc.nasa.gov/giovanni/ and from TROPOMI at https://www.temis.nl/airpollution/no2.php . Satellite observations of NH 3 concentrations are available at https://iasi.aeris-data.fr/nh3/ . Meteorology data from ERA5 are available at https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset and from the NCEI at https://www.ncei.noaa.gov/ . WRF-Chem outputs and processed air quality data generated in this study are publicly available via the Princeton archive at https://doi.org/10.34770/xtje-mj26 .

Code availability

Source code for the WRF-Chem model utilized in this study is available at https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html#WRF-Chem . All custom codes are direct implementation of standard methods and techniques as described in detail in the Methods.

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Acknowledgements

We acknowledge project support from the M.S. Chadha Center for Global India and the Center for Policy Research on Energy and the Environment in the School of Public and International Affairs at Princeton University. K.M.R.H. is supported by a NERC Independent Research Fellowship (MITRE; grant number NE/W007924/1). We acknowledge the Central and State Pollution Control Boards for making surface PM 2.5 pollution measurements available through the CAAQM and NAMP monitoring network. We thank D. Sharma and C. Nguyen for helping to collect the CAAQM surface air quality data for 2015–2022. We thank S. Smith for instructions on using the CEDS global emissions inventory, and C. Venkataraman and T. Ganguly for instructions on using the India national emissions inventories. We also thank F. Paulot, V. Naik, L.W. Horowitz and M. Lin for their suggestions early in this study. We thank M. Nambiar, R. Gupta, E. T. Downie, D. Chug, R. Chandra and W. Dong for constructive feedback on the manuscript.

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Center for Policy Research on Energy and Environment, School of Public and International Affairs, Princeton University, Princeton, NJ, USA

Yuanyu Xie, Mi Zhou & Denise L. Mauzerall

Department of Meteorology, University of Reading, Reading, UK

Kieran M. R. Hunt

National Centre for Atmospheric Sciences, University of Reading, Reading, UK

Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

Denise L. Mauzerall

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Contributions

Y.X. and D.L.M conceptualized the study. Y.X. retrieved and constructed the dataset and performed the analysis. M.Z. contributed to data processing, WRF-Chem model simulations and model evaluations. K.M.R.H. analysed the western disturbances. Y.X. and D.L.M. integrated the results and wrote the manuscript. All authors contributed to the interpretation of the findings, provided revisions to the manuscript and approved the final manuscript.

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Correspondence to Yuanyu Xie or Denise L. Mauzerall .

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Extended data

Extended data fig. 1 continuous pm monitoring stations in india..

( a ) Location of continuous PM monitoring stations from CAAQM (black) and the US AirNow (green) networks; ( b ) Comparison of daily mean PM 2.5 concentrations measured during 2017–2022 at five US AirNow monitoring sites and all CAAQM sites located within 5 km radius of the US AirNow sites. The correlation r 2 , normalized mean bias (NMB) and number (N) of PM 2.5 measurements are shown.

Extended Data Fig. 2 Continuous PM monitoring for each season.

Changes in the total number of NCAP non-attainment cities that had continuous PM monitoring from the CAAQM and US AirNow networks (bars, left axis) and number of total surface PM monitoring stations the CAAQM and US AirNow networks (lines, right axis) during 2017–2022 for ( a ) spring (MAM), ( b ) summer (JJA), ( c ) fall (SON) and ( d ) winter (DJF).

Extended Data Fig. 3 Continuous PM 10 monitoring stations in India.

( a ) Location of the 131 non-attainment cities (dots) and other cities with PM 10 monitoring (open blue circles) on the topographic map (in meters) over India. Blue indicates where continuous PM 10 monitoring is available from the CAAQM/US AirNow networks for at least one year during 2017–2022; black indicates no continuous PM10 monitoring is available from the CAAQM/US AirNow networks during 2017–2022; ( b ) Time series of annual mean PM 10 concentrations in 2017–2022 averaged in non-attainment cities with consecutive PM 10 data starting from each year during 2017–2021 (right axis); the left axis represent the ratio relative to 2017, the NCAP baseline; the number of cities with available consecutive PM 10 observations up to 2022 (numbers in parenthesis) are shown in different shades of grey; larger dots represent greater number of cities included for averaging; error bars denotes ±one standard error of means across available cities (n=13,17,27,36 for 2018–2021 as reported in parenthesis).

Extended Data Fig. 4 Annual and seasonal PM 2.5 pollution trends in Indian cities.

Time series of ( a ) annual and ( b-e ) seasonal (MAM-spring, JJA-summer, SON-fall, DJF-winter) mean PM 2.5 concentrations in 2017–2022 averaged in cities with consecutive PM 2.5 data starting from 2017 (black, number of cities reported in parenthesis), and for cities with consecutive data starting from 2018–2021 (different shades of grey; number of cities reported at the bottom); seasonal trends for non-attainment (all) cities is shown in black (orange); the left axis represent the ratio relative to 2017, the NCAP baseline; data starting from 2018–2021 are scaled to match with the ratio relative to 2017; larger dots represent greater number of cities included for averaging; error bars denotes ±one standard error of means across available cities (n=7,33,53,74,99 reported in panel a; n=15(17 for all cities),35,37,45,53 reported in panel b; n=11(12 for all cities),29,32,43,52 reported in panel c; n=13(14 for all cities),32,41,46,54 reported in panel d; n=28(36 for all cities),36,42,47,555 reported in panel e).

Extended Data Fig. 5 Observed decreases in surface PM 2.5 during 2017–2022 for each season.

Seasonal mean PM 2.5 measured at CAAQM and U.S. AirNow continuous monitoring sites during 2017–2022 for ( a ) March–May (MAM), ( b ) June–August (JJA), ( c ) September–November (SON), and ( d ) December–February (DJF). Note that DJF for 2017 represents December 2017 to February 2018. The larger dots with black circles represent PM 2.5 concentrations at non-attainment cities that are available for six consecutive years. The smaller dots without black circles represent PM 2.5 levels for cities without six consecutive years of data.

Extended Data Fig. 6 Changes in anthropogenic emissions over India from three global emission inventories.

( a-c ) Timeseries of anthropogenic emissions of SO 2 , NO x , NH 3 , OC, BC, PM 2.5 , PM 10 and NMVOC during 2000–2020 relative to 2017 from CEDS (v2021-04-21, left), EDGAR (v6.1, middle) and ECLIPSE (v6b, right). Data from ECLIPSE during 2019–2020 are projections. ( d ) comparison of annual total emissions in 2017 over India (Tg yr −1 ) from the three global emission inventories.

Extended Data Fig. 7 Changes in SO 2 , NO x and NH 3 during 2017–2022 over India.

( a ) Satellite observed total column SO 2 from OMI in India for 2017, 2022 and the difference between 2022 and 2017. ( b - c ) Same as (a) but for total column NO 2 from TROPOMI ( b ) and OMI ( c ) for 2018, 2022 and the difference between 2022 and 2018. ( d ) same as ( a ) but for total column NH 3 from IASI for 2017, 2022 and the difference between 2022 and 2017. Circles are surface observations of SO 2 ( a ), NO x ( b - c ) and NH 3 ( d ) for 2018, 2022 and the difference between 2022 and 2018.

Extended Data Fig. 8 Correlations between surface PM 2.5 and meteorological variables.

Correlation coefficient r between detrended daily surface PM 2.5 and meteorological variables for daily, 3-day, 5-day and 7-day averages in December–February during 2017–2022. From left to right: surface temperature (T2m), precipitation (Precip), relative humidity (RH), boundary layer height (BLH), surface pressure (Pressure), surface wind speed (WS-10m), temperature inversion between 925hPa and 2m (INV925-2m), temperature inversion between 850hPa and 2m (INV850-2m), 850hPa wind speed (WS-850), 500hPa wind speed (WS-500). Dots indicate statistical significance at 95 percentile confidence intervals.

Extended Data Fig. 9 Changes in meteorology in the winter of 2017 and 2021.

Differences in inversion ( a , b ), precipitation ( c , d , contour), wind speed at 10 meters ( e , f ) and transect of geopotential height and vertical-meridional circulation anomalies averaged between 73E–88E ( g , h ) in the winter of 2017 (left) and 2021 (right) relative to 2000–2022 mean. Tracking of western disturbance with average vorticity greater than 9e −5 m/s over northern India are each shown in c , d with different colors and shapes. Black shading in g , h indicates the surface topography along the transects.

Extended Data Fig. 10 Sensitivity simulations with changing emissions for the winter of 2017 and 2021.

( a ) Differences in the simulated surface PM 2.5 concentrations (in percent) in response to changes in emission alone (EMIS), meteorology alone (MET), and to changes in both emission and meteorology (EMIS+MET) relative to the baseline simulation with emissions and meteorology from 2017. Difference for emission perturbation simulations for 2021 (orange and dark blue) is compared to simulation with emissions from 2017 and meteorology from 2021. ( b ) same as ( a ) but for meteorological variables. The observed changes (OBS) in PM 2.5 and meteorological variables are shown as gray bars. Bars represent changes averaged from the 28 non-attainment cities shown in Fig. 4c, d , circles represent changes averaged from all 131 non-attainment cities.

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Xie, Y., Zhou, M., Hunt, K.M.R. et al. Recent PM 2.5 air quality improvements in India benefited from meteorological variation. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01366-y

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Evs Project On Air Pollution For Class 11th And 12th

Table of Contents

Acknowledgement:

I would like to take this opportunity to express my heartfelt gratitude to the individuals and organizations who have played a significant role in the completion of this Evs Project on Air Pollution. Their unwavering support, guidance, and contributions have been instrumental in the success of this endeavor.

First and foremost, I would like to extend my sincere appreciation to my teacher [mention teacher’s name] for providing me with valuable insights and guidance throughout the project. Their expertise and encouragement have been invaluable in shaping my understanding of the subject matter and guiding me in the right direction.

I am also grateful to [mention names of experts, environmentalists, or researchers] for their assistance and willingness to share their knowledge and experiences. Their valuable inputs during interviews, discussions, or consultations have enriched the project and provided a deeper understanding of the complexities surrounding air pollution.

I would like to acknowledge the support and cooperation received from [mention names of organizations or institutions]. Their provision of necessary resources, such as research materials, data, and access to facilities, has significantly contributed to the project’s comprehensiveness and credibility.

Furthermore, I extend my thanks to my friends and classmates who have been a constant source of encouragement and support throughout the project. Their feedback and constructive criticism have helped me refine my ideas and strengthen the project’s overall quality.

Lastly, I would like to express my gratitude to my family for their unwavering support and understanding during the project’s duration. Their encouragement, patience, and belief in my abilities have been vital in keeping me motivated and focused.

I acknowledge that this project would not have been possible without the collective effort and support of these individuals and organizations. Their contributions have truly made a difference in shaping this project on air pollution, and I am immensely grateful for their involvement.

Once again, I extend my heartfelt thanks to everyone who has played a role in this project. Your support and guidance have been invaluable, and I am grateful for the opportunity to work on such an important topic under your guidance.

Introduction:

Air pollution is a pressing environmental issue that has garnered significant attention worldwide due to its detrimental effects on human health, ecosystems, and the overall well-being of our planet. The continuous release of pollutants into the atmosphere from various sources has resulted in a degraded air quality that poses severe risks to both the environment and public health.  

The primary objective of this Evs Project on Air Pollution is to shed light on the seriousness of this issue and raise awareness among individuals, communities, and policymakers. By understanding the causes, consequences, and potential solutions related to air pollution, we can take proactive measures to address and mitigate its harmful effects.

Air pollution originates from multiple sources, both natural and human-induced. Natural sources include volcanic eruptions, wildfires, and dust storms, while human activities contribute significantly to the problem. Emissions from industries, power plants, vehicles, and improper waste disposal are among the primary culprits responsible for air pollution.

The consequences of air pollution are far-reaching and impact various aspects of our lives. It adversely affects human health, leading to respiratory problems, cardiovascular diseases, allergies, and even premature death. Additionally, air pollution has severe implications for ecosystems, harming plant and animal life and disrupting delicate ecological balances. Furthermore, it contributes to climate change by influencing the Earth’s radiation balance and exacerbating global warming.

Recognizing the urgency of the situation, it is essential to explore and implement effective measures to combat air pollution. Through this project, we aim to provide insights into the possible solutions and strategies that can be adopted at individual, community, and governmental levels. By promoting sustainable practices, advocating for stricter emission controls, and encouraging the use of clean energy sources, we can make significant progress in reducing air pollution levels and preserving the health of our planet.

By increasing awareness and understanding of air pollution, we can empower individuals to make informed choices and take actions that contribute to a cleaner and healthier future. This project serves as a platform to educate and inspire students, policymakers, and the general public to prioritize and actively engage in efforts to combat air pollution.

In conclusion, this Evs Project on Air Pollution aims to highlight the severity of the problem and emphasize the importance of addressing it promptly. By comprehending the causes and consequences of air pollution and exploring potential solutions, we can pave the way for a sustainable and healthier future for ourselves and future generations.

Evs Project on Air Pollution:

The Evs Project on Air Pollution is a comprehensive study that delves into the multifaceted aspects of air pollution. It encompasses an in-depth analysis of its causes, effects, and potential solutions. By conducting thorough research and gathering relevant data, this project seeks to enhance awareness among individuals, communities, and policymakers regarding the pressing need to tackle air pollution promptly and effectively.

One of the primary objectives of this project is to identify and examine the various causes of air pollution. It explores both natural and anthropogenic factors that contribute to the degradation of air quality. Natural causes include volcanic eruptions, dust storms, and pollen, while anthropogenic causes encompass emissions from industries, transportation, energy generation, and household activities. By understanding the root causes, this project highlights the need for comprehensive strategies to address and mitigate these sources of pollution.

Furthermore, the project investigates the wide-ranging effects of air pollution on the environment, public health, and climate change. It explores the detrimental impacts on ecosystems, including the depletion of biodiversity, disruption of ecological balance, and damage to vegetation. The project also emphasizes the severe health consequences for humans, such as respiratory illnesses, cardiovascular diseases, and impaired lung function. Additionally, it underscores the role of air pollution in exacerbating climate change by contributing to the greenhouse effect and altering weather patterns.

The Evs Project on Air Pollution goes beyond merely identifying the problems associated with air pollution. It aims to present potential solutions and strategies to mitigate this issue effectively. It explores both individual and collective actions that can be taken to reduce air pollution. These may include adopting sustainable transportation alternatives, promoting the use of clean energy sources, implementing stricter emission standards and regulations, advocating for effective waste management practices, and fostering public awareness and education on the importance of clean air.

By increasing awareness through this project, individuals, communities, and policymakers can be motivated to prioritize and take action against air pollution. It emphasizes the need for collaborative efforts involving government initiatives, industry practices, and individual responsibility to achieve substantial progress in addressing this environmental concern.

In conclusion, the Evs Project on Air Pollution aims to provide a comprehensive understanding of the causes, effects, and potential solutions to air pollution. By raising awareness and advocating for effective measures, this project seeks to empower individuals and communities to take proactive steps towards mitigating air pollution and safeguarding the well-being of the environment and future generations.

Examples of Air Pollution:

The section on examples of air pollution provides a detailed exploration of various sources that contribute to the deterioration of air quality. It focuses on highlighting specific instances or case studies related to air pollution, shedding light on their environmental and health impacts.

Industrial emissions are one of the prominent sources of air pollution. Factories and manufacturing facilities release a range of pollutants into the atmosphere, including particulate matter, sulfur dioxide, nitrogen oxides, and volatile organic compounds. These emissions can lead to smog formation, acid rain, and respiratory issues for nearby communities. For example, the industrial region of Norilsk in Russia has experienced severe air pollution due to metal smelting operations, resulting in significant environmental damage and adverse health effects on the local population.

Vehicular pollution is another major contributor to air pollution, particularly in urban areas. Exhaust emissions from automobiles release harmful pollutants like carbon monoxide, nitrogen dioxide, and particulate matter. Cities with high traffic congestion often experience elevated pollution levels and associated health problems. For instance, Delhi, the capital city of India, has witnessed severe air pollution due to the large number of vehicles on its roads, leading to respiratory ailments and reduced air quality indexes.

Indoor air pollution is a lesser-known but significant concern. Activities such as cooking with solid fuels like wood, coal, or biomass release harmful pollutants into indoor environments. This can have adverse effects on the health of individuals, especially women and children who are exposed to these pollutants for extended periods. In rural areas of developing countries, where clean cooking technologies are not readily available, indoor air pollution poses a serious health risk.

Agricultural activities, particularly the use of chemical fertilizers and pesticides, contribute to air pollution as well. The release of ammonia, pesticides, and other chemicals into the air can lead to smog formation and adversely affect air quality. This pollution can have detrimental effects on both human health and ecosystems.

The burning of fossil fuels, including coal, oil, and natural gas, is a significant source of air pollution globally. Power plants, residential heating systems, and industrial processes that rely on fossil fuel combustion emit greenhouse gases, sulfur dioxide, nitrogen oxides, and particulate matter. These pollutants contribute to climate change, smog formation, and respiratory diseases. For instance, the severe air pollution episodes witnessed in Beijing, China, were largely attributed to the burning of coal for heating and industrial purposes.

By examining these specific examples and case studies, the project aims to illustrate the diverse sources and impacts of air pollution. It emphasizes the urgency of addressing these sources through effective policies, technological advancements, and individual actions to safeguard the environment and public health.

Importance of Evs Project on Air Pollution:

The Evs Project on Air Pollution plays a vital role in today’s world by addressing one of the most pressing environmental challenges we face. It holds immense importance as it helps individuals, communities, and policymakers understand the gravity of the air pollution problem and its far-reaching consequences on ecosystems, human health, and climate change. By raising awareness through this project, it serves as a catalyst for inspiring action and driving changes in policies and personal behavior to effectively reduce air pollution.

Firstly, the project educates individuals about the detrimental effects of air pollution on ecosystems. It highlights how air pollutants can harm plant and animal life, disrupt ecological balances, and lead to the loss of biodiversity. By understanding these impacts, individuals gain a deeper appreciation for the interconnectedness of ecosystems and recognize the need to protect and preserve them.

Secondly, the project emphasizes the severe health implications of air pollution on human well-being. It sheds light on how air pollutants, such as particulate matter, ozone, and nitrogen dioxide, can contribute to respiratory problems, cardiovascular diseases, allergies, and even premature death. By creating awareness about these health risks, the project empowers individuals to prioritize their own well-being and take proactive measures to minimize exposure to air pollutants.

Furthermore, the Evs Project on Air Pollution addresses the critical link between air pollution and climate change. It highlights how certain pollutants, such as carbon dioxide and other greenhouse gases, contribute to global warming and the disruption of weather patterns. By understanding this connection, individuals recognize the urgency of reducing air pollution as part of the broader efforts to mitigate climate change and its associated impacts, such as rising sea levels, extreme weather events, and habitat loss.

Additionally, the project plays a crucial role in advocating for changes in policies and regulations. By raising awareness about the adverse effects of air pollution, it prompts individuals to engage with policymakers and demand stricter emission standards, increased investment in renewable energy sources, and sustainable urban planning. This project can contribute to the development and implementation of more effective environmental policies that prioritize air quality and protect public health.

Moreover, the Evs Project on Air Pollution encourages changes in personal behavior and lifestyle choices. By educating individuals about the sources of air pollution and their own contribution to it, the project promotes the adoption of sustainable practices. It inspires individuals to make conscious decisions such as reducing reliance on private vehicles, supporting clean energy alternatives, practicing proper waste management, and promoting indoor air quality.

In conclusion, the Evs Project on Air Pollution is of paramount importance in our world today. By raising awareness about the gravity of the problem, its detrimental effects on ecosystems, human health, and climate change, it inspires action and drives changes in policies and personal behavior to reduce air pollution. This project empowers individuals to make informed choices and actively contribute to creating a cleaner and healthier environment for ourselves and future generations.

How Can We Make It Happen?

This section explores practical steps and measures that can be taken to address air pollution. It discusses the importance of adopting sustainable transportation, promoting renewable energy sources, implementing stricter emission standards, encouraging waste management practices, and raising awareness among the general public. The focus is on individual and collective actions that can contribute to reducing air pollution.

The Three Pillars:

The three pillars of this project are:

Education and Awareness: This pillar emphasizes the need to educate individuals about the causes and impacts of air pollution. It promotes awareness campaigns, workshops, and educational programs to empower people with knowledge and encourage them to take action.

Policy and Regulation: This pillar emphasizes the importance of enacting and enforcing stringent policies and regulations to control air pollution. It discusses the role of government bodies, international agreements, and collaborations in formulating effective policies and implementing pollution control measures.

Technology and Innovation: This pillar focuses on the role of technology and innovation in combating air pollution. It explores advancements in clean energy technologies, air quality monitoring systems, and sustainable practices that can significantly reduce pollution levels.

Conclusion:

The Evs Project on Air Pollution serves as a catalyst for change, promoting awareness, sustainable practices, and policy advocacy to address the urgent issue of air pollution. By delving into the causes, effects, and potential solutions, this project empowers individuals, communities, and governments to take concerted action towards creating a cleaner and healthier future for ourselves and future generations.

Through this project, individuals gain a comprehensive understanding of the causes and effects of air pollution. Armed with knowledge, they can recognize the detrimental impact it has on ecosystems, human health, and climate change. This awareness fuels a sense of responsibility and urgency to take action against air pollution.

The project emphasizes the importance of collective efforts, urging individuals, communities, and governments to work together. By collaborating, sharing knowledge, and implementing sustainable practices, we can effectively combat air pollution. The project highlights the significance of initiatives such as adopting clean transportation alternatives, promoting renewable energy sources, implementing stricter emission regulations, and raising public awareness.

Furthermore, the Evs Project on Air Pollution underlines the importance of policy advocacy. It emphasizes the need for governments to enact and enforce stringent regulations and standards to control air pollution effectively. This includes collaboration on an international level to address transboundary pollution and foster sustainable practices across borders.

Ultimately, the project recognizes the shared responsibility of individuals, communities, and governments to protect our planet. By actively participating in efforts to reduce air pollution, we can contribute to the creation of a healthier environment for ourselves and future generations. It is crucial for us to recognize the interconnectedness of our actions and their impact on the planet.

In conclusion, the Evs Project on Air Pollution serves as a call to action, inspiring individuals, communities, and governments to work collectively towards combatting air pollution. By raising awareness, promoting sustainable practices, advocating for effective policies, and fostering collaboration, we can create a cleaner, healthier, and more sustainable future for all. It is our responsibility to protect and preserve our planet for current and future generations.

Certificate of Completion

This is to certify that I, [Student’s Name], a [Class/Grade Level] student, have successfully completed the project on “Evs Project On Air Pollution For Class 11th And 12th.” The project explores the fundamental principles and key aspects of the chosen topic, providing a comprehensive understanding of its significance and implications.

In this project, I delved into in-depth research and analysis, investigating various facets and relevant theories related to the chosen topic. I demonstrated dedication, diligence, and a high level of sincerity throughout the project’s completion.

Key Achievements:

Thoroughly researched and analyzed Evs Project On Air Pollution For Class 11th And 12th. Examined the historical background and evolution of the subject matter. Explored the contributions of notable figures in the field. Investigated the key theories and principles associated with the topic. Discussed practical applications and real-world implications. Considered critical viewpoints and alternative theories, fostering a well-rounded understanding. This project has significantly enhanced my knowledge and critical thinking skills in the chosen field of study. It reflects my commitment to academic excellence and the pursuit of knowledge.

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How does air pollution impact residence intention of rural migrants empirical evidence from the cmds.

1. Introduction

2. literature review, 3. theoretical analysis and research hypotheses, 3.1. direct effects, 3.2. moderating effects, 3.2.1. social networks, 3.2.2. social integration, 4. methods and models, 4.2. variables setting, 4.2.1. dependent variable, 4.2.2. independent variable, 4.2.3. instrumental variable, 4.2.4. control variables, 4.3. model construction, 4.3.1. baseline model, 4.3.2. moderating effect model, 5. empirical analysis, 5.1. baseline results, 5.2. robustness test, 5.2.1. endogenous treatment, 5.2.2. replace independent variable, 5.2.3. replace dependent variable, 5.2.4. replace regression model, 5.3. moderating effects analysis, 5.3.1. social networks, 5.3.2. social integration, 5.4. heterogeneity analysis, 5.4.1. geographical location, 5.4.2. precipitation.

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5.4.3. Environmental Regulation

5.4.5. human capital levels, 5.4.6. flow domain, 6. conclusions and suggestions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Zhang, C.; Zhang, G. How Does Air Pollution Impact Residence Intention of Rural Migrants? Empirical Evidence from the CMDS. Sustainability 2024 , 16 , 5784. https://doi.org/10.3390/su16135784

Zhang C, Zhang G. How Does Air Pollution Impact Residence Intention of Rural Migrants? Empirical Evidence from the CMDS. Sustainability . 2024; 16(13):5784. https://doi.org/10.3390/su16135784

Zhang, Chuanwang, and Guangsheng Zhang. 2024. "How Does Air Pollution Impact Residence Intention of Rural Migrants? Empirical Evidence from the CMDS" Sustainability 16, no. 13: 5784. https://doi.org/10.3390/su16135784

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particulate revelations —

These light paintings let us visualize invisible clouds of air pollution, world health organization: air pollution causes 7 million premature deaths per year.

Jennifer Ouellette - Jun 6, 2024 9:28 pm UTC

Light painting is a technique used in both art and science that involves taking long-exposure photographs while moving some kind of light source—a small flashlight, perhaps, or candles or glowsticks—to essentially trace an image with light. A UK collaboration of scientists and artists has combined light painting with low-cost air pollution sensors to visualize concentrations of particulate matter (PM) in select locations in India, Ethiopia, and Wales. The objective is to creatively highlight the health risks posed by air pollution, according to a new paper published in the journal Nature Communications.

“Air pollution is the leading global environmental risk factor," said co-author Francis Pope , an environmental scientist at the University of Birmingham in the UK who spearheaded the Air of the Anthropocene project with artist Robin Price. "[The project] creates spaces and places for discussions about air pollution, using art as a proxy to communicate and create dialogues about the issues associated with air pollution. By painting with light to create impactful images, we provide people with an easy-to-understand way of comparing air pollution in different contexts—making something that was largely invisible visible."

Light painting has been around since 1889, when Étienne-Jules Marey and Georges Demeny , who were investigating the use of photography as a scientific tool to study biological motion, created the first known light painting called Pathological Walk From in Front . In 1914, Frank and Lillian Mollier Gilbreth tracked the motion of manufacturing and clerical workers using light painting techniques, and in 1935, Man Ray "signed" his Space Writing series with a penlight—a private joke that wasn't discovered until 74 years later by photographer/historian Ellen Carey in 2009.

American photographer Barbara Morgan started making light paintings in the 1930s, capturing famous dancers like Martha Graham in motion. And Pablo Picasso was photographed for Life magazine in 1949 making impromptu sketches with a small flashlight, the most famous of which is entitled Picasso Draws a Centaur .

It's still a popular technique, driven by the 21st-century availability of dSLR cameras, portable light sources like LEDs, and smartphone cameras that enable real-time feedback to adjust light or exposure. For instance, in 2007 and 2008, LA-based artist Lia Halloran created a series of light paintings of skateboarders at night, dubbed " Dark Skate ." And Finnish artist Janne Parviainen created a striking series of light paintings in 2011, "Light Skeletons," in the snow, using fire as his source of light.

Pope et al.'s latest paper builds on the work of Steve Mann, who used digital light painting as a means of visualizing sensor data, and of Timo Arnall, who used it to visualize the strength of WiFi signals. The team thought it would be an ideal approach for the Art of the Anthropocene project. The World Health Organization has estimated that 99 percent of the Earth's population breathes at least some polluted air, causing some 7 million premature deaths every year. The problem is particularly severe in India and China, and African countries have seen sharp deterioration of air quality over the last 50 years as well.

• IIT Nursery Playground, Delhi, India: PM 2.5 500–600 micrograms per cubic meter Robin Price
• Indoor Biomass Burning Kitchen, Addis Ababa, Ethiopia: PM 2.5 150–200 micrograms per cubic meter. Robin Price
• Playground at the Institute of Himalayan Biotechnology, Palumper, India: PM 2.5 30–40 micrograms per cubic meter. Robin Price
• Prince Street air quality monitoring site, Port Talbot, Wales: PM 2.5 30–40 micrograms per cubic meter. Robin Price

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