Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.

Abstract

MEJ aims to create easy-to-use, publicly-available maps that paint a holistic picture of intersecting environmental, social, and health impacts experienced by communities across the US.

With guidance from the residents of impacted communities, MEJ combines environmental, public health, and demographic data into an indicator of vulnerability for communities in every state. MEJ’s goal is to fill an existing data gap for individual states without environmental justice mapping tools, and to provide a valuable tool for advocates, scholars, students, lawyers, and policy makers.

Methodology

The negative effects of pollution depend on a combination of vulnerability and exposure. People living in poverty, for example, are more likely to develop asthma or die due to air pollution. The method MEJ uses, following the method developed for CalEnviroScreen, reflects this in the two overall components of a census tract’s final “Cumulative EJ Impact”: population characteristics and pollution burden. The CalEnviroScreen methodology was developed through an intensive, multi-year effort to develop a science-backed, peer-reviewed tool to assess environmental justice in a holistic way, and has since been replicated by several other states.

CalEnviroScreen Methodology:

• Population characteristics are a combination of socioeconomic data (often referred to as the social determinants of health) and health data that together reflect a populations' vulnerability to pollutants. Pollution burden is a combination of direct exposure to a pollutant and environmental effects, which are adverse environmental conditions caused by pollutants, such as toxic waste sites or wastewater releases. Together, population characteristics and pollution burden help describe the disproportionate impact that environmental pollution has on different communities.

• Every indicator is ranked as a percentile from 0 to 100 and averaged with the others of the same component to form an overall score for that component. Each component score is then percentile ranked to create a component percentile. The Sensitive Populations component score, for example, is the average of a census tract’s Asthma, Low Birthweight Infants, and Heart Disease indicator percentiles, and the Sensitive Populations component percentile is the percentile rank of the Sensitive Populations score.

• The Population Characteristics score is the average of the Sensitive Populations component score and the Socioeconomic Factors component score. The Population Characteristics percentile is the percentile rank of the Population Characteristics score.

• The Pollution Burden score is the average of the Pollution Exposure component score and one half of the Environmental Effects component score (Environmental Effects may have a smaller effect on health outcomes than the indicators included the Exposures component so are weighted half as much as Exposures). The Pollution Burden percentile is the percentile rank of the Pollution Burden score.

• The Populaton Characteristics and Pollution Burden scores are then multiplied to find the final Cumulative EJ Impact score for a census tract, and then this final score is percentile-ranked to find a census tract's final Cumulative EJ Impact percentile.

• Census tracts with no population aren't given a Population Characteristics score.

• Census tracts with an indicator score of zero are assigned a percentile rank of zero. Percentile rank is then only calculated for those census tracts with a score above zero.

• Census tracts that are missing data for more than two indicators don't receive a final Cumulative EJ Impact ranking.

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MAPS OverviewThe MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry.Instrument The MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data.SRL-1 Mission GoalsThe MAPS SRL-1 mission took place during Northern Hemisphere Spring when global biomass burning does not typically occur. Some burning may occur for the purpose of clearing the damaged and felled trees in the forests of North America after the rather severe winter. The goals of the MAPS SRL-1 mission are to provide a validated, near-global atlas of the distribution of tropospheric Carbon Monoxide during the mission, and to assess the health status of the MAPS instrument as the mission progresses. SL1 SummaryHigh concentrations of carbon monoxide over the Northern Hemisphere can be seen in measurements made by the Measurement of Air Pollution from Space(MAPS) instrument. These April 1994 measurements, made from the Space Shuttle Endeavour(STS-59), show large sources of air pollution in the lower atmosphere (2 to 10 miles above the surface) over the industrialized Northern Hemisphere.The data that are available from MAPS SRL1 include a 5 by 5 degree gridded box (MAPS_SRL1_5X5_HDF) and a second by second data product (MAPS_SRL1_COSEC_HDF). These data sets are available from the Langley DAAC.

The total of air pollutants divided by the distance to each class I area, summed. This dataset is the core of the Air Pollution Web Application. This dataset was last updated in 2020.

MAPS Overview The MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into & three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data. The data that are available from MAPS OSTA3 include a 5 by 5 degree gridded box (MAPS_OSTA3_5X5_HDF) and a second by second data product (MAPS_OSTA3_COSEC_HDF). These data sets are available from the Langley DAAC.

SUMMARY The Air Pollutant Exposure Zone (APEZ) map identifies areas in San Francisco where air modeling indicates higher levels of air pollution. This map is required to be updated every 5 years, as established in San Francisco Health Code article 38. The 2025 Air Pollutant Exposure Zone map is an update to the 2020 map. Additional information may be found at Air Quality Review | SF Planning. The map can be viewed on the San Francisco Property Information Map. HOW THE DATASET IS CREATED The 2025 APEZ update modeled areas of the city where: particulate matter (PM2.5) is greater than or equal to 9 µg/m3 or where the risk of cancer from air pollutants is greater than or equal to 100 in a million; in health vulnerable ZIP codes (94102, 94103, 94110, 94124, and 94134), where the risk of cancer from air pollutants is greater than or equal to 90 in a million; locations within 500 feet of freeways; or locations within 1,000 feet of roadways with a daily average of 100,000 vehicles. To learn more, visit San Francisco Citywide Health Risk Assessment: Technical Support Documentation, Air Pollutant Exposure Zone Handout and Air Pollutant Exposure Zone Story Map. UPDATE PROCESS Updated every five years. HOW TO USE THIS DATASET The City uses this dataset as follows. San Francisco Health Code article 38 requires new developments or major renovations within the APEZ with sensitive receptors, like housing or preschools, to include a ventilation system that sufficiently removes fine particulate matter (minimum efficiency reporting volume [MERV] 13 or equivalent filtration). In addition, Environment Code Chapter 25 requires public agencies implementing projects within the APEZ to use the cleanest construction equipment available. The City’s environmental review under the California Environmental Quality Act (CEQA) uses the APEZ in its analysis to mandate the use of clean construction equipment, when applicable. To learn more, visit Air Quality Review | SF Planning.

The map shows annual mean concentrations of Particulate Matter (PM2.5) in Europe based on daily averages with at least 75% of valid measurements, in µg/m3 (source: EEA, AirBase v.8 & AQ e-Reporting)Thresholds used in the maps for annual values [µg/m3]:≤ 10: (10 μg/m3, as set out in the WHO air quality guideline for PM2.5)> 10 ≤ 20: (20 μg/m3, limit value as set out in the Air Quality Directive, 2008/50/EC)> 20 ≤ 25: (25 μg/m3, target value as set out in the Air Quality Directive, 2008/50/EC)> 25 ≤ 30> 30Source: AirBase v.8 & AQ e-ReportingAirBase is the European air quality database maintained by the EEA through its European topic centre on Air pollution and Climate Change mitigation. It contains air quality monitoring data and information submitted by participating countries throughout Europe.The air quality database consists of a multi-annual time series of air quality measurement data and statistics for a number of air pollutants. It also contains meta-information on those monitoring networks involved, their stations and their measurements.The database covers geographically all EU Member States, the EEA member countries and some EEA collaborating countries. The EU Member States are bound under Decision 97/101/EC to engage in a reciprocal exchange of information (EoI) on ambient air quality. The EEA engages with its member and collaborating countries to collect the information foreseen by the EoI Decision because air pollution is a pan European issue and the EEA is the European body which produces assessments of air quality, covering the whole geographical area of Europe.

Annually produced 1x1km background concentrations from Department for Environment, Food and Rural Affairs (Defra) national Pollution Climate (PCM) models. These are typically (but not uniquely) annual mean concentrations. The download webpage shows the years available for each pollutant and provides additional information on the relevant units/metrics. The model results are used in conjunction with measured concentrations from Defra's national monitoring networks to provide an air quality assessment that is reported to the European Commission in accordance with European Directives. The methodology and results are described in a mapping report published by Defra on UKAIR website. Note that these background maps are distinct from the source sector split concentration maps produced for Local Air Quality Management (LAQM) purposes and should not be used for LAQM operations.

Hyperlocal Air Pollution Mapping Market Outlook

According to our latest research, the global Hyperlocal Air Pollution Mapping market size reached USD 2.14 billion in 2024, driven by increasing urbanization and escalating concerns over air quality in metropolitan regions. The market is anticipated to register a robust CAGR of 13.9% from 2025 to 2033, reaching a forecasted value of USD 6.29 billion by 2033. This rapid growth is primarily attributed to the rising adoption of advanced sensor networks and data analytics, which are enabling cities and organizations to monitor air pollution at a granular, street-by-street level.

One of the primary growth factors for the Hyperlocal Air Pollution Mapping market is the increased awareness of the detrimental health effects of air pollution, particularly in densely populated urban areas. Governments and municipalities are under mounting pressure to implement policies and interventions aimed at reducing air pollution and protecting public health. The proliferation of low-cost, high-precision sensors, coupled with advancements in IoT connectivity, has made it feasible to deploy dense networks capable of capturing real-time air quality data at a hyperlocal scale. This granular data is instrumental in identifying pollution hotspots, informing policy decisions, and guiding urban planning efforts to mitigate exposure risks for vulnerable populations such as children and the elderly.

Another significant driver of market expansion is the integration of Hyperlocal Air Pollution Mapping solutions into broader smart city initiatives. As urban centers strive to become more sustainable and livable, the demand for actionable environmental intelligence has surged. Hyperlocal data not only supports targeted interventions, such as optimizing traffic flow or regulating industrial emissions, but also enhances public engagement by providing citizens with transparent, real-time information about the air they breathe. The synergy between mapping technologies, mobile monitoring, and cloud-based analytics platforms is fostering a new era of data-driven urban management, further propelling market growth.

The market is also benefiting from the increasing involvement of commercial and industrial stakeholders who recognize the value of hyperlocal air quality data for regulatory compliance, corporate social responsibility, and operational efficiency. Industries such as logistics, real estate, and healthcare are leveraging these insights to optimize site selection, reduce exposure risks for employees, and enhance their environmental stewardship. Furthermore, the collaboration between research institutes, environmental agencies, and technology providers is accelerating innovation in sensor technologies, data analytics, and visualization tools, ensuring that the market continues to evolve in response to emerging challenges and opportunities.

Regionally, North America and Europe are leading the adoption of hyperlocal air pollution mapping solutions, driven by stringent environmental regulations and well-established smart city frameworks. However, the Asia Pacific region is poised for the fastest growth, fueled by rapid urbanization, rising pollution levels, and significant investments in urban infrastructure modernization. Countries such as China and India are deploying large-scale sensor networks to address air quality crises, while governments in Southeast Asia are incorporating hyperlocal data into their urban planning and public health strategies. As the market matures, cross-sector collaboration and public-private partnerships will play a pivotal role in scaling hyperlocal air pollution mapping initiatives globally.

Component Analysis

The Hyperlocal Air Pollution Mapping market by component is segmented into hardware, software, and services, each playing a distinct and critical role in the ecosystem. Hardware forms the backbone of the market, encompassing an array of air quality sensors, data loggers, and communication modules that are deployed in urban, indust

Hyperlocal Air Pollution Mapping Market Outlook

According to our latest research, the Global Hyperlocal Air Pollution Mapping market size was valued at $1.2 billion in 2024 and is projected to reach $4.8 billion by 2033, expanding at a robust CAGR of 16.7% during the forecast period of 2025–2033. The surge in demand for real-time, granular air quality data to inform urban planning, public health interventions, and environmental policy is a major factor driving the growth of the hyperlocal air pollution mapping market globally. As cities grapple with rising pollution levels and stricter regulatory requirements, stakeholders are increasingly turning to advanced sensor networks, software analytics, and integrated monitoring solutions to gain actionable insights at the neighborhood and street level, fueling significant market expansion.

Regional Outlook

North America currently holds the largest share of the global hyperlocal air pollution mapping market, accounting for approximately 38% of total revenue in 2024. This dominance can be attributed to the region's mature technological ecosystem, high penetration of IoT and sensor-based networks, and proactive governmental policies aimed at environmental monitoring. The United States, in particular, has witnessed widespread deployment of fixed and mobile monitoring stations across urban centers, driven by both federal initiatives and local municipal programs. Additionally, strong collaborations between academia, private sector technology firms, and public agencies have accelerated innovation in data analytics and visualization platforms, further consolidating North America's leadership in this field.

The Asia Pacific region is emerging as the fastest-growing market, projected to register a CAGR of 20.3% between 2025 and 2033. Rapid urbanization, escalating air quality concerns in megacities like Beijing, Delhi, and Jakarta, and significant investments in smart city infrastructure are key drivers of growth. Governments across the region are implementing ambitious air quality monitoring projects, often in partnership with international organizations and tech startups. The proliferation of affordable sensor technologies and mobile-based monitoring solutions is enabling hyperlocal mapping even in resource-constrained settings, while rising public awareness and policy mandates are accelerating adoption among municipal authorities and environmental agencies.

In emerging economies across Latin America, the Middle East, and Africa, adoption of hyperlocal air pollution mapping technologies is gaining momentum, albeit at a slower pace due to infrastructural and budgetary constraints. Localized demand is primarily driven by urban centers experiencing acute pollution episodes, where public health concerns and international donor support are catalyzing pilot projects. However, challenges such as limited technical expertise, inconsistent regulatory frameworks, and gaps in data infrastructure persist, potentially hampering widespread deployment. Nevertheless, as environmental policy reforms take hold and technology costs decline, these regions are expected to contribute increasingly to the global market, particularly through localized innovation and public-private partnerships.

Report Scope

In this dataset we publish maps of modelled air pollution (PM2.5, PM10, NOx and NO2) and noise (expressed as Lden) for the base modelled years 2000, 2011, and 2018. For NO2 and Lden we have total levels, and for the other air pollutants we have both total levels, and also the levels from the different major local sources, traffic exhaust, traffic road wear and resuspension, shipping, residential heating, and other (a mixture of non-road machinery, agricultural sources and diffuse area sources).

AI‑Enhanced Air Pollution Mapping Market Outlook

According to our latest research, the AI-Enhanced Air Pollution Mapping market size reached USD 2.9 billion globally in 2024, driven by the urgent need for real-time, high-resolution air quality data and the proliferation of advanced AI technologies. The market is projected to grow at a robust CAGR of 16.8% from 2025 to 2033, reaching an estimated USD 13.2 billion by the end of the forecast period. This impressive growth is underpinned by increasing urbanization, rising health concerns related to air pollution, and the adoption of AI-powered solutions by governments and industries worldwide. As per our latest research, the integration of machine learning, deep learning, and computer vision into environmental monitoring solutions is transforming the accuracy and efficiency of air pollution mapping, making it a critical tool for urban planning, industrial emissions tracking, and public health interventions.

The primary growth factor for the AI-Enhanced Air Pollution Mapping market is the rising global awareness of the health risks posed by poor air quality. Governments and public health agencies are under mounting pressure to provide real-time air quality data to their populations, especially in densely populated urban areas where pollution levels can fluctuate rapidly. AI-powered air pollution mapping systems enable authorities to collect, process, and visualize vast amounts of environmental data from sensors, satellites, and mobile devices, providing actionable insights for timely interventions. Furthermore, the increasing frequency of wildfires, industrial accidents, and transboundary air pollution events has highlighted the need for advanced monitoring systems that can predict and track pollution plumes with high spatial and temporal resolution. These factors collectively drive the adoption of AI-enhanced solutions, fostering a dynamic and rapidly expanding market landscape.

Another significant growth driver is the evolution of regulatory frameworks and environmental policies worldwide. Many countries are implementing stricter air quality standards and emission reduction targets, compelling both public and private sector stakeholders to invest in advanced monitoring technologies. The availability of funding and incentives for smart city initiatives and sustainable urban development projects has accelerated the deployment of AI-enhanced air pollution mapping solutions. Additionally, the integration of AI with Internet of Things (IoT) devices, drones, and satellite imagery allows for comprehensive, multi-source data collection and analysis, further improving the accuracy and coverage of pollution maps. These technological advancements not only support compliance with regulations but also enable predictive analytics for proactive pollution management, which is increasingly valued by policymakers and environmental organizations.

The market’s growth is also fueled by the expanding application of AI-enhanced air pollution mapping in industrial and commercial sectors. Industrial facilities, particularly in high-emission industries such as manufacturing, energy, and transportation, are leveraging AI-driven solutions to monitor their environmental impact in real-time and optimize emission control strategies. Environmental organizations and research institutes are utilizing advanced analytics and machine learning models to study pollution patterns, assess the effectiveness of mitigation measures, and support advocacy efforts. The convergence of AI, big data, and cloud computing has democratized access to sophisticated air quality monitoring tools, enabling even smaller organizations and municipalities to deploy scalable and cost-effective solutions. As these technologies mature, their adoption is expected to become ubiquitous across various end-user segments, further propelling market growth.

From a regional perspective, the AI-Enhanced Air Pollution Mapping market demonstrates strong momentum in Asia Pacific, North America, and Europe, with each region exhibiting unique growth drivers and challenges. Asia Pacific leads in market share due to its rapid urbanization, large population centers, and persistent air quality challenges, particularly in countries like China and India. North America benefits from advanced technological infrastructure, strong regulatory frameworks, and significant investments in smart city projects, while Europe’s market is characterized by stringent environmental

AI‑Enhanced Air Pollution Mapping Market Outlook

According to our latest research, the Global AI‑Enhanced Air Pollution Mapping market size was valued at $1.2 billion in 2024 and is projected to reach $6.8 billion by 2033, expanding at a robust CAGR of 20.3% during the forecast period of 2025–2033. The rapid proliferation of IoT sensors and growing urbanization are major drivers, as cities worldwide increasingly rely on real-time, AI-powered air pollution mapping solutions for effective environmental management and public health protection. The integration of advanced artificial intelligence technologies with traditional air quality monitoring systems is fundamentally transforming the landscape, enabling granular pollution tracking, predictive analytics, and actionable insights for governments, industries, and citizens alike. This surge in demand is further propelled by heightened public awareness, stringent environmental regulations, and the urgent need to address climate change and urban health risks.

Regional Outlook

North America holds the largest market share in the AI‑Enhanced Air Pollution Mapping market, accounting for approximately 38% of the global revenue in 2024. This dominance is attributed to the region’s early adoption of AI-driven technologies, mature digital infrastructure, and strong regulatory frameworks that mandate air quality monitoring and reporting. The United States, in particular, has seen significant investments from both public and private sectors, with agencies such as the Environmental Protection Agency (EPA) leveraging AI to enhance pollution tracking and mitigation strategies. Additionally, a high concentration of leading technology providers and robust R&D activity further bolster North America’s leadership position. The presence of several smart city initiatives and the integration of AI-based solutions into urban planning have also accelerated the deployment of advanced air pollution mapping platforms across major metropolitan areas.

The Asia Pacific region is anticipated to witness the fastest growth, registering a remarkable CAGR of 24.1% between 2025 and 2033. This surge is driven by rapid urbanization, escalating industrial emissions, and increasing government focus on combating air pollution in countries such as China, India, and South Korea. Massive investments in smart city projects and the proliferation of low-cost IoT sensors are enabling real-time, city-wide air quality monitoring. Furthermore, international collaborations, public-private partnerships, and substantial funding for environmental technology startups are catalyzing market expansion. The region’s growing awareness of health risks associated with poor air quality and the adoption of AI-enhanced solutions for predictive analytics and policy formulation are expected to sustain this high growth trajectory throughout the forecast period.

In emerging economies across Latin America, the Middle East, and Africa, market adoption is comparatively nascent but rapidly evolving. While these regions currently account for a smaller share of the global market, localized demand is rising due to increasing urban population densities, industrialization, and the impact of climate change on air quality. However, challenges such as limited digital infrastructure, budgetary constraints, and regulatory fragmentation can hinder widespread implementation of AI-enhanced air pollution mapping solutions. Nevertheless, targeted policy reforms, international aid, and pilot projects in major cities are gradually paving the way for broader adoption. As technology costs decline and awareness grows, these regions are poised to become important contributors to the global market, particularly as governments prioritize sustainable urban development and public health initiatives.

Report Scope

China has recently made available hourly air pollution data from over 1500 sites, including airborne particulate matter (PM), SO2, NO2, and O3. We apply Kriging interpolation to four months of data to derive pollution maps for eastern China. Consistent with prior findings, the greatest pollution occurs in the east, but significant levels are widespread across northern and central China and are not limited to major cities or geologic basins. Sources of pollution are widespread, but are particularly intense in a northeast corridor that extends from near Shanghai to north of Beijing. During our analysis period, 92% of the population of China experienced >120 hours of unhealthy air (US EPA standard), and 38% experienced average concentrations that were unhealthy. China’s population-weighted average exposure to PM2.5 was 52 μg/m3. The observed air pollution is calculated to contribute to 1.6 million deaths/year in China [0.7–2.2 million deaths/year at 95% confidence], roughly 17% of all deaths in China.

MAPS Overview The MAPS experiment measures the global distribution of carbon monoxide (CO) mixing ratios in the free troposphere. Because of MAPS' previous flights on board the Space Shuttle, Earth system scientists now know that carbon monoxide concentrations in the troposphere are highly variable around the planet, and that widespread burning in the South American Amazon Basin and southern cerrados, the African savannahs,and the Australian grasslands and ranches are major sources of carbon monoxide in the southern hemisphere and tropical troposphere.The 1994 flights of the MAPS experiment provided CO measurements that show seasonal changes in CO emissions, sources, transports, and chemistry.InstrumentThe MAPS instrument is based on a technique called gas filter radiometry. Thermal energy from the Earth passes through the atmosphere and enters the viewport of the downlooking MAPS instrument. Carbon monoxide and nitrous oxide (N2O) in the atmosphere produce unique absorption lines in the transmitted energy. The energy which enters the MAPS instrument is split into three beams. One beam passes through a cell containing CO and falls onto a detector. This CO gas cell acts as a filter for the effects of CO present in the middle troposphere. A second beam falls directly onto a detector without passing through any gas filter. The difference in the voltage of the signals from these two detectors can be used to determine the amount of CO present in the atmosphere at an altitude of 7-8 km. During the dedicated Earth-Observing Space Shuttle mission in 1994, MAPS measured the distribution of carbon monoxide in the middle troposphere to evaluate CO sources and chemistry, and to evaluate the seasonal and interannual variation of this key atmospheric trace gas. Interpretation of these measurements will help us to better understand the atmosphere and the consequences that human activities initiate in global climate change. A third beam of the incident energy passes through a cell containing N2O and falls onto a detector. This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data. SRL2 GoalsThe MAPS SRL-2 mission took place during the Northern Hemisphere summer when global biomass burning is nearing its maximum. The southern hemispheric burning of savanna and agricultural grasslands can be extensive in central and southern South America and in nearly all of Africa, south of the equator. The tundra regions of the northern boreal zone also are approaching the peak burning season. Other regions may experience scattered fire events as a result of lightning strikes during severe thunderstorms. The primary goal of the MAPS experiment on SRL-2 is to provide a near global survey of the distribution of tropospheric carbon monoxide during northern hemisphere summer. The secondary goal is to determine how the global distribution of carbon monoxide changes over the course of the mission.SL2 SummaryThe high values of carbon monoxide are associated with extensive areas of smoke and haze that have been observed by the Endeavour (STS-68) flight crew. The smoke results from fires that are burning in the continental regions. The carbon monoxide is carried by tropical thunderstorms to the altitudes (2 to 10 miles above the surface) at which it is measured by the MAPS instrument.The data that are available from MAPS SRL2 include a 5 by 5 degree gridded box (MAPS_SRL2_5X5_HDF) and a second by second data product (MAPS_SRL2_COSEC_HDF). These data sets are available from the Langley DAAC.

This layer contains the location of air pollution sites in Buffalo Niagara.

Globally available, ON-DEMAND noise pollution maps generated from real-world measurements (our sample dataset) and AI interpolation. Unlike any other available noise-level data sets! GIS-ready, high-resolution visuals for real estate platforms, government dashboards, and smart city applications.

U.S. EPA Air Data web mapping application contains points which depict air quality monitors within EPA's Air Quality System (AQS) monitoring network. The layers are updated weekly to reflect the most recent changes in the monitoring network. The monitors are generally operated by State, local, and tribal air pollution control agencies using procedures specified by the U.S. EPA. These agencies collect the data, quality assure it, and then submit it to the EPA Air Quality System (AQS). The layers include monitor information and links to download historic air quality data at each monitor. Layers in this web map show active and inactive monitors from these monitoring networks:CO - ActiveCO - InactiveLead - ActiveLead - InactiveLead - TSP(LC) - ActiveLead - TSP(LC) - InactiveLead - PM10(LC) - ActiveLead - PM10(LC) - InactiveNO2 - ActiveNO2 - InactiveOzone - ActiveOzone - InactivePM10 - ActivePM10 - InactivePM2.5 - NAAQS/AQI - ActivePM2.5 - NAAQS/AQI - InactivePM2.5 - Additional AQI - ActivePM2.5 - Additional AQI - InactiveSO2 - ActiveSO2 - InactivePM2.5 Chemical Speciation Network - ActivePM2.5 Chemical Speciation Network - InactiveIMPROVE (Interagency Monitoring of Protected Visual Environments) - ActiveIMPROVE (Interagency Monitoring of Protected Visual Environments) - InactiveNATTS (National Air Toxics Trends Stations) - ActiveNATTS (National Air Toxics Trends Stations) - InactiveNCORE (Multipollutant Monitoring Network) - ActiveNCORE (Multipollutant Monitoring Network) - InactiveNear Road - ActiveNear Road - InactivePAMS - ActivePAMS - InactiveAdditional layers are included to provide context:Nonattainment Areas:Lead, 2008Ozone, 2008Ozone, 1997PM10, 1987PM2.5, 2012PM2.5, 2006PM2.5, 1997SO2, 2010American Indian Reservations and Off-Reservation Trust LandsClass 1 areas under the Clean Air Act

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. The files below contain a map of Canada showing the locations of all facilities that reported direct releases to surface waters to the NPRI. The data are for the most recent reporting year, by reported total quantities of these releases. The map is available in both ESRI REST (to use with ARC GIS) and WMS (open source) formats. For more information about the individual reporting facilities, a dataset is available in a CSV format. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html