High-accuracy full-coverage PM 2.5 retrieval from 2014 to 2023 over China based on satellite remote sensing and hierarchical deep learning model DOI Creative Commons
Yulong Fan, Lin Sun, Xirong Liu

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Sept. 23, 2024

Language: Английский

Evaluation of Long-Term Performance of Six PM2.5 Sensor Types DOI Creative Commons
Karoline K. Barkjohn,

Robert Yaga,

B Thomas

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1265 - 1265

Published: Feb. 19, 2025

From July 2019 to January 2021, six models of PM2.5 air sensors were operated at seven quality monitoring sites across the U.S. in Arizona, Colorado, Delaware, Georgia, North Carolina, Oklahoma, and Wisconsin. Common PM sensor data issues identified, including repeat zero measurements, false high outliers, baseline shift, varied relationships between monitor, relative humidity (RH) influences. While these are often easy identify during colocation, they more challenging or correct deployment since it is hard differentiate real pollution events malfunctions. Air may exhibit wildly different performances even if have same similar internal components. Commonly used RH corrections still variable bias by hour day seasonally. Most show promise achieving Environmental Protection Agency (EPA) performance targets, findings here can be improve their reliability further. This evaluation generated a robust dataset colocated monitor data, making publicly available along with results presented this paper, we hope will an asset community understanding validating new methods.

Language: Английский

Citations

0
Influence of seasonal variation on spatial distribution of PM2.5 concentration using low-cost sensors DOI
Sandeep Kumar Chaudhry,

Sachchida Nand Tripathi,

T. V. Ramesh Reddy

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(12)

Published: Nov. 21, 2024

Citations

2
High-Accuracy Pm2.5 Retrieval Based on Satellite Remote Sensing and Hierarchical Machine Learning Model DOI
Yulong Fan, Lin Sun, Xirong Liu

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

1
High-accuracy full-coverage PM 2.5 retrieval from 2014 to 2023 over China based on satellite remote sensing and hierarchical deep learning model DOI Creative Commons
Yulong Fan, Lin Sun, Xirong Liu

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Sept. 23, 2024

Language: Английский

Citations

0