Drivers of PM 2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China
Environmental Pollution, ISSN: 0269-7491, Vol: 309, Page: 119777
2022
- 21Citations
- 10Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Fine particulate matter (PM 2.5 ) is a major source of air pollution in China. Although there have been many studies of the drivers of PM 2.5 pollution in the megacities clustered in eastern China, the behavior of PM 2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015–2019 obtained from the national air environmental monitoring network to examine variation in PM 2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM 2.5 in the study region. The annual average PM 2.5 concentrations over the study period was 54.3 μg/m 3, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320–370 d and 150–200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO 2, NO 2, relative humidity, sunshine duration and temperature. The two most primary variables, NO 2 and SO 2, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO 2 and other factors had the strongest effect on PM 2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM 2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0269749122009915; http://dx.doi.org/10.1016/j.envpol.2022.119777; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85134466086&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/35839968; https://linkinghub.elsevier.com/retrieve/pii/S0269749122009915; https://dx.doi.org/10.1016/j.envpol.2022.119777
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know