Dataset contains information on New York City air quality surveillance data.

Air pollution is one of the most important environmental threats to urban populations and while all people are exposed, pollutant emissions, levels of exposure, and population vulnerability vary across neighborhoods. Exposures to common air pollutants have been linked to respiratory and cardiovascular diseases, cancers, and premature deaths. These indicators provide a perspective across time and NYC geographies to better characterize air quality and health in NYC. Data can also be explored online at the Environment and Health Data Portal: http://nyc.gov/health/environmentdata.

Street Noise-Level Dataset — Regulatory & Governmental Use

Silencio’s Street Noise-Level Dataset provides regulatory bodies, governmental agencies, and public health authorities with the most reliable and detailed data on environmental noise worldwide. Built from over 35 billion datapoints, collected via our mobile app and enhanced through AI-powered interpolation, this dataset covers hyper-local average noise levels (dBA) across streets, neighborhoods, and cities in over 200 countries.

Our dataset is specifically suited for noise regulation, environmental impact assessments, policy-making, and compliance monitoring. It offers objective, real-world acoustic data that goes beyond traditional noise models by combining actual user-collected measurements with AI-predicted values. Authorities can use the data to map noise pollution hotspots, monitor changes over time, enforce regulations, and inform sustainable urban and environmental strategies.

Silencio also operates the world’s largest noise complaint database, giving policymakers a unique tool to correlate objective noise exposure with subjective community reports for more people-focused decision-making.

Delivery options are flexible, including: • CSV exports • S3 bucket access • High-resolution image maps suitable for integration into reports, GIS platforms, or public communication.

The dataset is available both as historical records and continuously updated data. An API is currently in development, and we are open to early access discussions and custom integrations tailored to government and regulatory workflows.

Fully anonymized and fully GDPR-compliant, Silencio’s data supports transparent, evidence-based environmental and public health decision-making.

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.

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.

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. 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.

Spatial Analysis of Urban Noise Pollution: We conducted a spatial analysis of the distribution of noise pollution across HOLC grades for 83 cities in the United States (data in HOLC_Noise_City_results.csv). To be included in the study, the city needed to be included in both datasets used in the analysis: 1) the Mapping Inequality Project dataset on the distribution of HOLC grades across cities, and 2) the U.S. Department of Transportation, National Transportation Noise Map 2018. Any cities in which the distribution of HOLC grades did not include all four grades (A-D) were excluded from the analysis, which largely excluded cities with population sizes below 100,000 people. To evaluate noise exposure across HOLC grades for each city in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map (2018). The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models. For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to summarize the median noise levels and area covered by excess noise (i.e., values > 35 dBA). We used the resulting zonal statistics estimates and the formula from Collins et al. (2019) to calculate an area-corrected measure of excess noise:

N = (r * Md)/a

where N is excess noise in each HOLC grade (with units of dBA/900m2); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Thus, N represents a measure of both the level of noise and the area covered by excess noise in a given HOLC grade for each city.

Literature Review on the Impacts of Noise to Urban Wildlife: To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson’s ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted of Shannon et al.’s search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of ‘Acoustics’, ‘Zoology’, ‘Ecology’, ‘Environmental Sciences’, ‘Ornithology’, ‘Biodiversity Conservation’, ‘Evolutionary Biology’, and ‘Marine Freshwater Biology’. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set (n = 207). We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in of Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise (n = 46). We then verified whether there were significant biological responses to a particular noise level threshold, noting each noise level if multiple biological responses were recorded. We recorded responses to noise into one of eight possible biological response categories, many of which were taken or modified from the biological response categories utilized in Shannon et al. (2016). The following were the biological response categorical values: movement behavior, vocal behavior, physiological, population, mating behavior, foraging behavior, vigilance behavior, life history / reproduction, and ecosystem. For any new articles published since the Shannon et al. (2016) dataset (n = 354) or those published between 1990 and 2013 but not reviewed by Shannon et al. (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria (n = 161) and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. All terrestrial papers used a reference pressure of 20 microPascals (μPa). Due to the low sample size of aquatic studies (n = 4), differences in reference pressures, and varying sound intensities amongst aquatic studies, we only included terrestrial studies in statistical analyses and figures. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper, but were unable to convert the various sound metrics given to a single sound metric for standardization during analysis. Thus, there were various sound metrics used in the analysis of the data extracted from the literature search, in particular for the cumulative weight-of-evidence curve, which poses a limitation in the comparison of noise levels amongst papers. Additionally, we recorded the weightings for each noise level, with many of the papers being A-weighted (dBA; n = 100) and Z-weighted (dBZ; n = 4). These weightings relate to typical characteristics of sounds as observed by humans. Many papers, however, did not record the weighting and/or the exact sound metric used.

Pollution Impact Potential (PIP) maps were generated separately for nitrate and phosphate to rank critical source areas (CSAS) relative to one from diffuse agriculture for both the Groundwater and surface water receptor. The PIP maps are generated by the EPA Catchment Characterisation Tool (CCT). The CCT delineates the CSAS displayed in the PIP maps by overlaying the hydro(geo)logically susceptible areas (the likelihood of nutrient transfer due to soil and geological properties along the near surface and/or subsurface Pathway) with nitrate or phosphate loadings. The nitrate and phosphate PIP maps for the surface water receptor combine the contribution from both the subsurface Pathway and the near surface Pathway while the Groundwater receptor maps only consider the contribution from the Groundwater Pathway. Localised Groundwater Nitrate PIP map shows the relative pollution impact potential to Groundwater along the subsurface Pathway due to nitrate loading taking dilution into account. This map should be used to evaluate local drinking water issues in wells. Pollution impact potential (PIP) maps rank the CSAS in Descending order of risk (where Rank 1 is the highest risk) and are available for the surface water receptor for nitrate and phosphate, and the Groundwater receptor for nitrate. Local pressure data has been used to generate the maps in agricultural areas where available. For urban, Forestry and the remaining agricultural areas, regional sources of pressure data have been used; these areas are marked ‘using regional loadings’ on the PIP maps.

This systematic evidence map (SEM) examines and characterizes peer-reviewed evidence on urban-level policy interventions to reduce traffic emissions and/or TRAP from on-road mobile sources, thus potentially reducing human exposures and adverse health effects and producing various co-benefits.

The objective the systematic evidence map (SEM) is to examine and characterize the evidence on urban-level policy interventions that can be implemented by urban authorities to reduce traffic emissions and/or traffic-related air pollution (TRAP) from on-road mobile sources, thus potentially reducing human exposures and adverse health impacts. We created this open access, query-able database to facilitate the identification of relevant trends and gaps in the evidence base and serve as the foundation for future research and a reference for practice and policy recommendations. The database contains information for 376 articles and 1,139 policy scenarios within. There are 58 unique policy interventions documented which fall under one of the following overarching policy categories: 1) pricing, 2) land-use, 3) infrastructure, 4) behavioral, 5) technology, and 6) management, standards, and services. Urban authorities, such as cities, air agencies, local authorities including county and district councils, and metropolitan planning organizations (MPOs) or districts, are encouraged to use this database to identify information on various urban-level policy interventions implemented around the world to reduce traffic emissions and TRAP, and potentially yield benefits to human exposures and health and a wide range of documented social, environmental, climate and economic outcomes.

Noise maps by street section show the noise levels that reach the facades on average, between two street intersections, according to the type of source and the time period. A Strategic Noise Map (SNM) is a set of maps that serve to globally assess the population's exposure to noise produced by different noise sources in a given area, and to serve as the basis for the development of action plans. They are used as a management tool to fight noise pollution and are developed every 5 years. This information can be graphically consulted in the Environmental data maps. The other available datasets from Strategic Noise Map can also be consulted. For further details about the Strategic Noise Map, check the FAQ of Environmental data maps.

This is air quality data is from Center Pollution Control Board(CPCB). It has both historic and near real time data. Dataset has more than 100 stations data distributed all across India.

The air quality in Beijing, especially its PM2.5 level, has become of increasing public concern because of its importance and sensitivity related to health risks. A set of monitored PM2.5 data from 31 stations, released for the first time by the Beijing Environmental Protection Bureau, covering 37 days during autumn 2012, was processed using spatial interpolation and overlay analysis. Following analyses of these data, a distribution map of cumulative exceedance days of PM2.5 and a temporal variation map of PM2.5 for Beijing have been drawn. Computational and analytical results show periodic and directional trends of PM2.5 spreading and congregating in space, which reveals the regulation of PM2.5 overexposure on a discontinuous medium-term scale. With regard to the cumulative effect of PM2.5 on the human body, the harm from lower intensity overexposure in the medium term, and higher overexposure in the short term, are both obvious. Therefore, data of population distribution were integrated into the aforementioned PM2.5 spatial spectrum map. A spatial statistical analysis revealed the patterns of PM2.5 gross exposure and exposure probability of residents in the Beijing urban area. The methods and conclusions of this research reveal relationships between long-term overexposure to PM2.5 and people living in high-exposure areas of Beijing, during the autumn of 2012.

This data set mainly includes five folders comprised of the following: 1. Cover Map Folder: It contains data named Cover Map*.jpg format; 2. Landsat 8 OLI Remote Sensing Image Map (True Color Composition) Folder of Sanya City: It contains data in Landsat 8 OLI Remote Sensing Image Map (True Color Composition)*.jpg format; 3. Data Processing Flow Chart Folder: It contains data named Data Processing Flow Chart*. vsdx format. 4. The folder of annual thermal anomaly extraction results of Sanya City from 2008 to 2017: annual thermal anomaly extraction results of Sanya City in 2008-2017*.png format; 5. Metadata folder of annual thermal anomaly extraction results in Sanya City from 2008 to 2017: metadata of annual thermal anomaly extraction results in Sanya City in 2008-2017*.shp format; 6. Code folder: contains the *. py format code used in the data processing process; 7. Sanya Vector Boundary Folder: Sanya City Boundary Data with *. SHP format.

This layer quantifies the yearly economic value of removal of atmospheric particulate matter 2.5 (PM 2.5) by trees. The forests of Maryland play an important role in reducing air pollution in the state. Trees remove pollutants from the air by absorption through leaf stomata and interception by leaves. The forest soil is also a large and important sink for air pollutants. This ecosystem service is especially important due to its effect on human health. The pollutants removed from the air by trees can have many negative effects on human health, causing or exacerbating bronchitis, cardiovascular stress, and asthma. This effect is greater in urban areas, due to the combination of there being more people to benefit and higher concentrations of air pollution in urban areas. This ecosystem service totals $120,014 for the state yearly.

This data layer was created as part of the Maryland Department of Natural Resources "Accounting for Maryland's Ecosystem Services" program.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://mdgeodata.md.gov/imap/rest/services/Environment/MD_EcosystemServices/MapServer/9Download the Ecosystem Services layers at: https://www.dropbox.com/s/e6ovfcc01dxvnmo/EcosystemServices.gdb.zip?dl=0

This EnviroAtlas dataset demonstrates the effect of changes in pollution concentration on local populations in 612 block groups in Salt Lake City, UT. The US EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) was used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) and associated monetary value that result from changes in pollution concentrations for Salt Lake City and County, UT. Incidence and value estimates for the block groups are calculated using i-Tree models (www.itreetools.org), local weather data, pollution data, and U.S. Census derived population data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments. Methods We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative data. We followed these procedures for both collection cycles (2020 during lockdown and 2021 after lockdown had been lifted). The 2017 data were collected for an unrelated noise pollution project conducted by previous members of the Primack Lab and were not collected with the exact parameters established for the 2020 and 2021 collections. However, we found these noise data to be valuable given that they could be used to compare lockdown sound levels to the soundscape before the COVID-19 pandemic. We used R Studio to create sound maps from the individual data points in a way that allows for spatial visualization of the soundscape before, during, and after the pandemic lockdown. To test for statistically significant differences in sound level between years, we performed Welch’s t-tests on the raw data for all sites comparing lockdown (2020) measurements to pre (2017) and post (2021) lockdown measurements. Given the hypothesis that 2020 would have lower sound levels at each site, we report the results of one-tailed t-tests.

This EnviroAtlas dataset demonstrates the effect of changes in pollution concentration on local populations in 6,378 block groups in New York City, New York. The US EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) was used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) and associated monetary value that result from changes in pollution concentrations for Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, and Summit Counties, OH. Incidence and value estimates for the block groups are calculated using i-Tree models (www.itreetools.org), local weather data, pollution data, and U.S. Census derived population data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset presents environmental benefits of the urban forest in 128 block group in New Bedford, Massachusetts. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

In this paper, we are presenting databases and results for 20 Indian cities. Due to space constraints and to avoid repetition, several figures and tables are not included in the main text. The Supplementary contains the following (a) for 20 cities - selected urban airsheds as google KML files showing 0.01º grids (b) for 20 cities - urban-rural built area maps, emission projections for PM2.5 between 2015 and 2030, gridded PM2.5 emission maps, modeled PM2.5 concentration maps, and modeled monthly variation of PM2.5 concentrations for the selected airshed (c) for 20 cities – the WRF modeled wind speed, wind direction, temperature, precipitation and mixing height for the airshed as annual summary figures, summary table showing monthly variation, and database of hourly values (d) for all India – list of number of operational ambient monitoring stations and recommendation number of monitoring stations by state (37) and by district (640) (e) for all India – daily average ambient monitoring data for the period of 2011-2015 from all the stations operational under the national ambient monitoring programme (f) for all India – summary of satellite data + global model based surface PM2.5 estimates for the period of 1998 and 2016 by state (37) and by district (640) and (g) for 20 cities – summary of WRF-CAMx modeled PM2.5 source apportionment.

This layer quantifies the yearly removal of atmospheric particulate matter 10 (PM 10) by trees. The forests of Maryland play an important role in reducing air pollution in the state. Trees remove pollutants from the air by absorption through leaf stomata and interception by leaves. The forest soil is also a large and important sink for air pollutants. This ecosystem service is especially important due to its effect on human health. The pollutants removed from the air by trees can have many negative effects on human health, causing or exacerbating bronchitis, cardiovascular stress, and asthma. This effect is greater in urban areas, due to a higher concentrations of air pollution in urban areas.

This data layer was created as part of the Maryland Department of Natural Resources "Accounting for Maryland's Ecosystem Services" program.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://mdgeodata.md.gov/imap/rest/services/Environment/MD_EcosystemServices/MapServer/6Download the Ecosystem Services layers at: https://www.dropbox.com/s/e6ovfcc01dxvnmo/EcosystemServices.gdb.zip?dl=0