The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change
Open accessAbstract
Abstract We applied a multiple linear regression (MLR) model to study the correlations of total PM2.5 and its components with meteorological variables using an 11-year (1998–2008) observational record over the contiguous US. The data were deseasonalized and detrended to focus on synoptic-scale correlations. We find that daily variation in meteorology as described by the MLR can explain up to 50% of PM2.5 variability with temperature, relative humidity (RH), precipitation, and circulation all being important predictors. Temperature is positively correlated with sulfate, organic carbon (OC) and elemental carbon (EC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. RH is positively correlated with sulfate and nitrate, but negatively with OC and EC. Precipitation is strongly negatively correlated with all PM2.5 components. We find that PM2.5 concentrations are on average 2.6 μg m−3 higher on stagnant vs. non-stagnant days. Our observed correlations provide a test for chemical transport models used to simulate the sensitivity of PM2.5 to climate change. They point to the importance of adequately representing the temperature dependence of agricultural, biogenic and wildfire emissions in these models.
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Self-citationPark, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M., & Chin, M. (2004) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2003jd004473 · Cited by 987
Self-citationSpatial analysis of air pollution and mortality in Los Angeles
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Cited by
Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China
Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., & Bates, K. H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812168116 · Cited by 1,804
Self-citationAir Quality and Climate Connections
Fiore, A. M., Naik, V., & Leibensperger, E. M. (2015) · Journal of the Air & Waste Management Association · doi:10.1080/10962247.2015.1040526 · Cited by 537
Quantifying the influence of local meteorology on air quality using generalized additive models
Pearce, J. L., Beringer, J., Nicholls, N., Hyndman, R. J., & Tapper, N. J. (2011) · Atmospheric Environment · doi:10.1016/j.atmosenv.2010.11.051 · Cited by 334
Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., & Zhang, M. (2017) · Atmospheric Environment · doi:10.1016/j.atmosenv.2017.02.023 · Cited by 258
The relationship between teleworking, traffic and air pollution.
Giovanis, E. (2018) · Atmospheric Pollution Research · doi:10.1016/j.apr.2017.06.004 · Cited by 143
Metadata
- Authors
- Tai, A. P. K., Mickley, L. J., & Jacob, D. J.
- Venue
- Atmospheric Environment
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 44
- Issue
- 32
- Pages
- 3976-3984
- ISSN
- 1352-2310 (ISSN-L)
- OpenAlex
- W2034446988
- Cited by
- 1,048
- References
- 38
- FWCI
- 11.24
- Type
- journal article
- Language
- en
- Open access
- green
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- Tai, A. P. K., Mickley, L. J., & Jacob, D. J. (2010). Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2010.06.060
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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