Dataset Information

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.

ABSTRACT: Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R²) value of 0.740 for PM_2.5, 0.754 for CO and 0.716 for SO₂. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

SUBMITTER: Guo R

PROVIDER: S-EPMC9265361 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.

Guo Rong R Qi Ying Y Zhao Bu B Pei Ziyu Z Wen Fei F Wu Shun S Zhang Qiang Q

International journal of environmental research and public health 20220629 13

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality mon ...[more]

PMID: 35805664

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Project description:The novel coronavirus (COVID-19) has induced unprecedented improvements of air quality due to drastic shrinking of human activities during the pandemic lockdown in 2020. While declines of most air pollutants have been globally evidenced in most cities worldwide, there is few detailed spatial knowledge at local scale. Therefore, we present here a high resolution mapping of the 2018-2020 evolution of human activities and air pollutants in Wuhan. Human activities were assessed by nighttime light radiance. We measured the air quality index (AQI) as the maximum value among air quality sub-indices of SO2, NO2, CO, O3 and particulate matter. We also compared mean monthly pollutant concentration during January-April in 2018, 2019 and 2020. Mapping results show that variations of nighttime light radiance were heterogenous at local scale, showing both rises and declines in the same district. The radiance decreased in eight districts located mostly in the city center, as a result of lower human activity, but the radiance increased in the five surrounding districts, as a consequence of people staying at home. AQI was low during lockdown, averaging at 57, but showed strong daily variations with a slight pollution around February 5 with AQI rising to 126. During this pollution event, particulate matter, SO2, NO2 and CO levels were positively correlated, suggesting common sources, but were not correlated with ozone; and particulate matter, SO2, NO2 and CO decreased with relative humidity, suggesting removal by precipitation. Comparison of 2020 data with previous years shows that particulate matter and NO2 were highly reduced, CO was less reduced due to ongoing power industries, SO2 first declined then increased to exceed 2018-19 values due to coal combustion, and ozone levels was more abundant due both to less NOx pollution and the weekend effect.Supplementary informationThe online version contains supplementary material available at 10.1007/s10311-021-01222-x.

Dataset Information

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.

Publications

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.

Similar Datasets

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