Spatial and seasonal patterns of particulate matter less than 2.5 microns in the Sierra Nevada Mountains, California

Cisneros, Ricardo; Schweizer, Don; Preisler, Haiganoush K.; Bennett, Deborah H.; Shaw, Glenn D.; Bytnerowicz, Andrzej

doi:10.5094/apr.2014.067

Cited by 18 publications

(11 citation statements)

References 22 publications

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“…The above exercise, using this tool, seems to indicate that wildland fire is not a major driving force in attainment compliance of the federal standard in this area. This is similar to the findings in Cisneros et al (2014) where the majority of the PM 2.5 pollution in the Sierra Nevada appears to be driven by the constant anthropogenic emissions, rather than the episodic natural sources, like fire.…”

Section: Discussionsupporting

confidence: 87%

A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke

Preisler

Schweizer

Cisneros

et al. 2015

Environmental Pollution

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Section: Discussionsupporting

confidence: 87%

A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke

Preisler

Schweizer

Cisneros

et al. 2015

Environmental Pollution

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“…Our findings are consistent with many studies [46,47]. Geomor and CR likely affect PM 2.5 agglomeration and diffusion by affecting air flow, air pressure, temperature and precipitation [26,46]. There are several possible explanations for this finding.…”

Section: Analysis Of the Natural Drivers Of Pm 25 Pollutionsupporting

confidence: 92%

“…This result demonstrates large-scale spatial heterogeneity and indicates that geomorphology and climate regionalization have an important influence on the agglomeration and diffusion of PM 2.5 [20]. Our findings are consistent with many studies [46,47]. Geomor and CR likely affect PM 2.5 agglomeration and diffusion by affecting air flow, air pressure, temperature and precipitation [26,46].…”

Section: Analysis Of the Natural Drivers Of Pm 25 Pollutionsupporting

confidence: 90%

PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

Yun

Jiang

et al. 2019

Atmosphere

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High concentrations of PM2.5 are a primary cause of haze in the lower atmosphere. A better understanding of the spatial heterogeneity and driving factors of PM2.5 concentrations is important for effective regional prevention and control. In this study, we carried out remote sensing inversion of PM2.5 concentration data over a long time series and used spatial statistical analyses and a geographical detector model to reveal the spatial distribution and variation characteristics of PM2.5 and the main influencing factors in the Yangtze River Delta from 2005 to 2015. Our results show that (1) The average annual PM2.5 concentration in the Yangtze River Delta prior to 2007 displayed an increasing trend, followed by a decreasing trend after 2007 which eventually stabilized; and (2) climate regionalization and geomorphology were the dominant natural factors driving PM2.5 concentration diffusion, while total carbon dioxide emissions and population density were the dominant socioeconomic factors affecting the formation of PM2.5. Natural factors and socioeconomic factors together lead to PM2.5 pollution. These findings provide an interpretation of PM2.5 spatial distribution and the mechanisms influencing PM2.5 pollution, which can help the Chinese government develop effective abatement strategies.

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“…eBAMs (Model 9800, MetOne, Grants Pass, OR), which work by beta attenuation method for the measurement of PM 2.5 mass concentration ( Cheng et al., 2008 ; Cisneros et al., 2014 ; Schweizer et al., 2016 ) eBAMs were deployed in North and South sites. eBAMs are relatively mobile instruments and intended for temporary deployment, although have shown good correlation (R 2 = 0.9 for daily mean) with non-mobile federal equivalent method BAM instruments ( Schweizer et al., 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions

Kumar

Sreekanth

Salmon

et al. 2018

Environmental Pollution

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This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM2.5) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM2.5 concentrations during June 2015–May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM2.5 concentration was ∼30 μg m−3 (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m−3) during 2–5% of observation days. Average concentrations were ∼25 μg m−3 higher during winter than during monsoon and ∼8 μg m−3 higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations.

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Spatial and seasonal patterns of particulate matter less than 2.5 microns in the Sierra Nevada Mountains, California

Cited by 18 publications

References 22 publications

A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke

A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke

PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions

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