African Air Quality (PM2.5) Predictions Date Selector

Name: African Air Quality (PM2.5) Predictions Date Selector
Creator: Zindi
License: 暂无描述

Esri Aid & Development Team2026-03-28 收录

下载链接：

https://cartong-esriaiddev.opendata.arcgis.com/maps/NRF-SAEON::african-air-quality-pm2-5-predictions-date-selector

下载链接

链接失效反馈

官方服务：

资源简介：

According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics. In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite. Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach. We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at <a href='https://github.com/johnowhitaker/air_quality_prediction' rel='nofollow ugc' style='text-decoration:none;'>https://github.com/johnowhitaker/air_quality_prediction</a>. For access to the original data, see <a href='https://search.datacite.org/works/10.15493/sarva.301020-2' rel='nofollow ugc' style='font-family: "Avenir Next", Avenir, "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 15px;' target='_blank'>https://search.datacite.org/works/10.15493/sarva.301020-2</a>.

提供机构：

Zindi

5,000+

优质数据集

54 个

任务类型

进入经典数据集