Daily and Hourly Surface PM2.5 Estimation From Satellite AOD

Abstract

Surface particulate matter (PM2.5, i.e., particulate matter with a median diameter of 2.5 µm or less) is one of the main air pollutants that has a significant negative effect on human health. Exposure to PM2.5 has been associated with many negative health outcomes including acute cardiovascular morbidity and mortality, respiratory mortality, hypertension, lung cancer, etc, and can also reduce people's life expectancy (e.g., Brook et al., 2010; L. Miller & Xu, 2018; Pope et al., 2009). While the anthropogenic PM2.5 concentrations are on the decline in the United States (U.S.) due to pollution controls implemented by the Environmental Protection Agency (EPA) and the daily average standard of 35 µg/m 3 is never met unless the concentrations are high (100 s of µg/m 3) due to smoke from fires or dust events. In parts of Asia, while de-seasonalized surface PM2.5 values are decreasing by about 2-8 µg/m 3 per year, health impacts continue to be profound due to high pollution levels (Liang et al., 2020; Zhai et al., 2019). Shi et al. (2018) report that between 1999 and 2014, there was a net 38% increase in premature deaths in Asia due to PM2.5 exposure. While monitoring network in the U.S. is dense in urban regions, there are gaps in some rural areas, especially in the central plains and mid-west (www.airnow.gov, accessed November 17, 2020). Elsewhere in the world, the coverage is especially sparse. The network of ground monitors across the world though is spreading with the use of low cost sensors (openaq.org, accessed November 17, 2020). Until the quality of the data gathered from these low cost sensors is assured, attempts to use satellite derived aerosol optical depth (AOD) to estimate surface PM2.5 that can alleviate the spatial coverage problem associated with ground measurements will continue (R. M. Hoff & Christopher 2009; van Donkelaar et al., 2010). Many methods have been developed to estimate surface PM2.5 from AOD (Chu et al., 2016), such as the simple linear regression methods (Engel

Distribution of Respiratory Tract Infectious Diseases in Relation to Particulate Matter (PM2.5) Concentration in Selected Urban Centres in Niger Delta Region of Nigeria

“…They established a strong relationship between air pollutants including particulate matter with morbidities sources such as respiratory diseases. Zhang and Kondragunta [46] found that high PM 2.5 concentration was associated with an increase in emergency department and out-patient units visit for respiratory diseases in children which is in uniform with the findings of this study.…”

“…In other studies, it was said that Particulate Matter exposure leads to respiratory diseases [10,13] . Also, Zhang and Kondragunta [46] concluded that higher PM 2.5 concentration was associated with an increase in emergency department and out-patients units visits for respiratory diseases, while Weli and Emeneke [33] concluded that residents in Port Harcourt which are sensitive to PM 2.5 especially those with respiratory diseases like COPD must not be allowed to spend longer hours or reside in those part of the city.…”

COVID‐19 Induced Fingerprints of a New Normal Urban Air Quality in the United States

“…The Suomi NPP VIIRS AOD product has been extensively validated by comparing it to Aerosol Robotic Network (AERONET) AODs and the VIIRS 550 nm AOD is shown to have a global bias of −0.046 ± 0.097 for AODs over land less than 0.1 and for AODs between 0.1 and 0.8, the bias is −0.194 ± 0.322. In the US, for VIIRS AODs ranging between 0.1 and 0.8, the bias is −0.008 ± 0.089 and for AODs greater than 0.8, the bias is about 0.068 ± 0.552 (Zhang & Kondragunta, 2021). For the analysis of AOD data in this study, we remapped the high quality (Quality Flag equals 0) 750 m resolution AOD retrievals to 0.05° x 0.05° resolution with a criterion that for a grid to have a mean AOD value, there should be a minimum of 20% 750 m pixels with high‐quality AODs.…”

Estimation and Analysis of PM2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China

“…Remote sensing data can be used to monitor the geographic phenomena of continuous ground surfaces for a long time. It has been widely used in PM 2.5 concentration monitoring [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Kahn et al [ 16 ] found that the particle size corresponding to the aerosol optical depth (AOD), obtained by the MISR inversion of the multiangle imaging spectrometer, was similar to the PM 2.5 particle size, which proved the feasibility of establishing the correlation model between AOD and PM 2.5 .…”