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.

The available data are fetched from http://pollution.gov.np by crawling and extracting web data. The activities of the fetching, cleaning, and publishing are done by the automated software bot and depend completely upon the quality of data published on the government websites, OKN does not guarantee the quality & validity of the data.

The National Air Pollution Surveillance (NAPS) program is the main source of ambient air quality data in Canada. The NAPS program, which began in 1969, is now comprised of nearly 260 stations in 150 rural and urban communities reporting to the Canada-Wide Air Quality Database (CWAQD). Managed by Environment and Climate Change Canada (ECCC) in collaboration with provincial, territorial, and regional government networks, the NAPS program forms an integral component of various diverse initiatives; including the Air Quality Health Index (AQHI), Canadian Environmental Sustainability Indicators (CESI), and the US-Canada Air Quality Agreement. Once per year, typically autumn, the Continuous data set for the previous year is reported on ECCC Data Mart. Beginning in March of 2020 the impact of the COVID-19 pandemic on NAPS Operations has resulted in reduced data availability for some sites and parameters. For additional information on NAPS data products contact the NAPS inquiry centre at RNSPA-NAPSINFO@ec.gc.ca Last updated March 2023. Supplemental Information Monitoring Program Overview The NAPS program is comprised of both continuous and (time-) integrated measurements of key air pollutants. Continuous data are collected using gas and particulate monitors, with data reported every hour of the year, and are available as hourly concentrations or annual averages. Integrated samples, collected at select sites, are analyzed at the NAPS laboratory in Ottawa for additional pollutants, and are typically collected for a 24 hour period once every six days, on various sampling media such as filters, canisters, and cartridges. Continuous Monitoring Air pollutants monitored continuously include the following chemical species: • carbon monoxide (CO) • nitrogen dioxide (NO2) • nitric oxide (NO) • nitrogen oxides (NOX) • ozone (O3) • sulphur dioxide (SO2) • particulate matter less than or equal to 2.5 (PM2.5) and 10 micrometres (PM10) Each provincial, territorial, and regional government monitoring network is responsible for collecting continuous data within their jurisdiction and ensuring that the data are quality-assured as specified in the Ambient Air Monitoring and Quality Assurance/Quality Control Guidelines. The hourly air pollutant concentrations are reported as hour-ending averages in local standard time with no adjustment for daylight savings time. These datasets are posted on an annual basis. Integrated Monitoring Categories of chemical species sampled on a time-integrated basis include: • fine (PM2.5) and coarse (PM10-2.5) particulate composition (e.g., metals, ions), and additional detailed chemistry provided through a subset of sites by the NAPS PM2.5 speciation program; • semi-volatile organic compounds (e.g., polycyclic aromatic hydrocarbons such as benzo[a]pyrene); • volatile organic compounds (e. g., benzene) The 24-hour air pollutant samples are collected from midnight to midnight. These datasets are generally posted on a quarterly basis. Data Disclaimer NAPS data products are subject to change on an ongoing basis, and reflect the most up-to-date and accurate information available. New versions of files will replace older ones, while retaining the same location and filename. The ‘Data-Donnees’ directory contains continuous and integrated data sorted by sampling year and then measurement. Pollutants measured, sampling duration and sampling frequency may vary by site location. Additional program details can be found at ‘ProgramInformation-InformationProgramme’ also in the data resources section. Citations National Air Pollution Surveillance Program, (year accessed). Available from the Government of Canada Open Data Portal at open.canada.ca.

This file describes the dataset used in Ou et al., "Air pollution control strategies directly limiting national health damages in the US." This work used the Global Change Assessment Model (GCAM) with state-level representation of the U.S. energy system (GCAM-USA). GCAM and GCAM-USA are developed and released by the University of Maryland/Pacific Northwest National Laboratory Joint Global Change Research Center (JGCRI). For further details, see the GCAM documentation: jgcri.github.io/gcam-doc. The model source code is available at github.com/JGCRI/gcam-core. A modified version of GCAMv4.3 was used for this analysis. Source code and input data specific for this paper are available upon request. This dataset contains Excel spreadsheets and an R script that link to comma-separated values (CSV) files that were extracted from the model output. The spreadsheets and scripts show the data and reproduce each of the figures in the paper. This dataset is associated with the following publication: Ou, Y., J. West, S. Smith, C. Nolte, and D. Loughlin. Air pollution control strategies directly limiting national health damages in the US.. Nature Communications. Nature Publishing Group, London, UK, 11: 957, (2020).

This is the dataset for the paper Deciphering Environmental Air Pollution with Large Scale City Data published in IJCAI 2022 in the AI for Good Track. It received a Best Paper award in the track. [Github Repo]

The dataset introduces a large scale spatio-temporal dataset involving all the major actors in urban air pollution. The dataset combines multiple sources for obtaining the information essential for studying urban air pollution - the pollutants themselves, traffic in the city, pollution from power generation industries and meteorological factors. All of these features are collected and curated on a daily level for 50+ cities in the United States over a 2 year period.

Relevant Columns:

Date: Date of the sample City: City of the sample X_median: Median value of the pollutant/meteorological feature X for the day mil_miles: Total vehicle travel distance for the sample pp_feat: Calculated feature for the influence of neighboring power plants Population Staying at Home: Used a measure of domestic emissions.

Pollutants: PM2.5,PM10,NO2,O3,CO,SO2

Meteorological Features: Temperature,Pressure,Humidity,Dew,Wind Speed,Wind Gust

Daily air quality data collected by the EPA Air Quality Service (AQS), from 1990-2021. This dataset includes air quality statistics from AQS monitors in the area surrounding Cambridge (Kenmore, Roxbury, Von Hillern, Chelsea). Each contains a parameter code which specifies one of the six pollutants for which the EPA AQS has an Air Quality Index (AQI).

Information on how to interpret AQI values can be found here: https://www.airnow.gov/aqi/aqi-basics/

The datasets contains date- and state-wise historically compiled data on air quality (by pollution level) in rural and urban areas of India from the year 2015 , as measured by Central Pollution Board (CPCB) through its daily (24 hourly measurements, taken at 4 PM everyday) Air Quality Index (AQI) reports.

The CPCB measures air quality by continuous online monitoring of various pollutants such as Particulate Matter10 (PM10), Particulate Matter2.5 (PM2.5), Sulphur Dioxide (SO2), Nitrogen Oxide or Oxides of Nitrogen (NO2), Ozone (O3), Carbon Monoxide (CO), Ammonic (NH3) and Lead (Pb) and calculating their level of pollution in the ambient air. Based on the each pollutant load in the air and their associated health impacts, the CPCB calculates the overall Air Pollution in Air Quality Index (AQI) value and publishes the data. This AQI data is then used by CPCB to report the air quality status i.e good, satisfactory, moderate, poor, very poor and severe, etc. of a particular location and their related health impacts because of air pollution.

'Visual Pollution'

## Overview

'Visual Pollution' is a dataset for object detection tasks - it contains GRAFFITI FADED SIGNGE POTHILES GARBAGE CONSTRUCTION ROAD BROKEN_SIGNAGE BAD_STREETLIGHT BAD BILLBOARD SAND ON ROAD CLUTTER_SIDEWALK UNKEPT_FACADE annotations for 7,715 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Annual emissions of various air pollutants in the United States have experienced dramatic reductions over the past half a century. As of 2023, emissions of nitrogen oxides (NOx) had reduced by more than 70 percent since 1970 to 6.8 million tons. Sulfur dioxide (SO₂) emissions have also fallen dramatically in recent decades, dropping from 23 million tons to 1.6 million tons between 1990 and 2023. Air pollutants can pose serious health hazards to humans, with the number of air pollution related deaths in the U.S. averaging 60,000 a year.

marine material spillage international oceans

The Transport Operations (Marine Pollution) Act 1995 and regulations protect Queensland's marine and coastal environment by minimising deliberate and negligent discharges of ship-sourced pollutants into coastal waters.

Under the Transport Operations (Marine Pollution) Act 1995 the master of a ship must report a discharge or probable discharge of any pollutant without delay to Maritime Safety Queensland or the Australian Maritime Safety Authority. Pollutants are defined as harmful substances and includes oil, chemicals, and sewage and garbage. Even minor instances of marine pollution must be reported.

The data files below contain reported marine pollution or suspected marine pollution in coastal waters.

For a full breakdown of each column in this dataset please refer to the supporting document – Field Descriptions.

This csv file provides air pollution data information for Florida and Districts for 2017, 2018, 2019 and 2020. Through the FDOT Source Book Special Edition 2020 report, users can drill down the air pollution data at the statewide and District level. The report's link is: https://sourcebook-2020-se-fdot.hub.arcgis.com/Florida remains within acceptable EPA standards for ozone concentration and fine particulate matter (PM 2.5).Data source: Environmental Protection Agency (EPA) Air Data. For any additional information, please contact the Forecasting and Trends Office (FTO) at 850-414-5396.

An estimated 2.8 billion people are exposed to hazardous levels of air pollution worldwide, representing almost 40 percent of the global population. The countries with the largest share of their populations exposed to hazardous concentrations of air pollution are Bangladesh, Nepal, and India, at more than 96 percent. Overall, roughly 94 percent of the global population are exposed to air pollution levels considered unsafe by the World Health Organization.

Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2019 (years 1-11) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: nyc-ehs.net/nyccas

The average annual air pollution level of PM2.5 in Delhi was over 102 µg/m³ in 2023, the highest among megacities in the Asia-Pacific region. In comparison, Nagoya's average annual air pollution level of PM2.5 was 9.5 µg/m³ in 2023.

Multiple linkages connect air quality and climate change. Many air pollutant sources also emit carbon dioxide (CO2), the dominant anthropogenic greenhouse gas (GHG). The two main contributors to non-attainment of U.S. ambient air quality standards, ozone (O3) and particulate matter (PM), interact with radiation, forcing climate change. PM warms by absorbing sunlight (e.g., black carbon) or cools by scattering sunlight (e.g., sulfates) and interacts with clouds; these radiative and microphysical interactions can induce changes in precipitation and regional circulation patterns. Climate change is expected to degrade air quality in many polluted regions by changing air pollution meteorology (ventilation and dilution), precipitation and other removal processes, and by triggering some amplifying responses in atmospheric chemistry and in anthropogenic and natural sources. Together, these processes shape distributions and extreme episodes of O3 and PM. Global modeling indicates that as air pollution programs reduce SO2 to meet health and other air quality goals, near-term warming accelerates due to “unmasking” of warming induced by rising CO2. Air pollutant controls on CH4, a potent GHG and precursor to global O3 levels, and on sources with high black carbon (BC) to organic carbon (OC) ratios could offset near-term warming induced by SO2 emission reductions, while reducing global background O3 and regionally high levels of PM. Lowering peak warming requires decreasing atmospheric CO2, which for some source categories would also reduce co-emitted air pollutants or their precursors. Model projections for alternative climate and air quality scenarios indicate a wide range for U.S. surface O3 and fine PM, although regional projections may be confounded by interannual to decadal natural climate variability. Continued implementation of U.S. NOx emission controls guards against rising pollution levels triggered either by climate change or by global emission growth. Improved accuracy and trends in emission inventories are critical for accountability analyses of historical and projected air pollution and climate mitigation policies.

Implications: The expansion of U.S. air pollution policy to protect climate provides an opportunity for joint mitigation, with CH4 a prime target. BC reductions in developing nations would lower the global health burden, and for BC-rich sources (e.g., diesel) may lessen warming. Controls on these emissions could offset near-term warming induced by health-motivated reductions of sulfate (cooling). Wildfires, dust, and other natural PM and O3 sources may increase with climate warming, posing challenges to implementing and attaining air quality standards. Accountability analyses for recent and projected air pollution and climate control strategies should underpin estimated benefits and trade-offs of future policies.

This is a PBL module on Air Pollution to be used in an introductory environmental science course to motivate students to analyze related environmental justice issues.US EPA data on "State EJScreen Data at the Block Group Level" (EJSCREEN_2023_BG_StatePct_with_AS_CNMI_GU_VI.csv) was downloaded from https://www.epa.gov/ejscreen/download-ejscreen-data on December 20, 2023.This data was processed/cleaned using the included R script - major changes (see the R script for full changes):The dataset was subset for the state of North Carolina.Only a subset of the variables were retained.Any row with missing information was removed. This reduced the number of rows, for North Carolina, from 7111 to 6494.Columns with percent values were converted from decimals to percentages.An additional, summarized dataset, summarized by county, is provided: we report the sum for ACSTOTPOP, AREALAND, and AREAWATER, and the mean for the other variablesThe data dictionary is included.PBL Module: TBAInstructor Notes: TBAThe following files are included:Data Dictionary: Data_Dictionary_EJSCREEN_2023_BG_Columns.pdfProcessed Dataset for North Carolina: DS4EJ_EJScreen_State_BGLevel_NC.csvSummarized Dataset for North Carolina (summarized by county): DS4EJ_EJScreen_State_BGLevel_NC_Summarized_By_County.csvR Script for Data Processing: DSEJ_data_cleaning_EJSCREEN.rOriginal Dataset from which Data was Extracted for North Carolina: DS4EJ_EJSCREEN_2023_BG_StatePct_with_AS_CNMI_GU_VI.csv

This dataset contains Riyadh Air Quality for 2019 - 2020. Data from The General Authority of Meteorology & Environmental Protection. Follow datasource.kapsarc.org for timely data to advance energy economics research.