Quantifying the influence of local meteorology on air quality using generalized additive models

Pearce, John L.; Beringer, Jason; Nicholls, Neville; Hyndman, Rob J.; Tapper, Nigel

doi:10.1016/j.atmosenv.2010.11.051

Cited by 266 publications

(157 citation statements)

References 23 publications

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“…Although quantitative contributions of different sources (e.g., coal burning and automobile exhaust) to airborne pollutants remain controversial, meteorological influences on airborne pollutants have been examined in depth by more and more scholars. Recent studies conducted in different countries indicated that PM 2.5 concentrations were closely related to temperature (Pearce et al, 2011;Yadav et al, 2014;Grundstrom et al, 2015), wind speed (Galindo et al, 2011;ElMetwally and Alfaro, 2013;Yadav et al, 2014) and precipitation (Yadav et al, 2014). Meanwhile, meteorological influences on PM 2.5 concentrations across China have also become a hot research topic.…”

Section: Introductionmentioning

confidence: 99%

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

et al. 2018

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Abstract. With frequent air pollution episodes in China, growing research emphasis has been put on quantifying meteorological influences on PM 2.5 concentrations. However, these studies mainly focus on isolated cities, whilst meteorological influences on PM 2.5 concentrations at the national scale have not yet been examined comprehensively. This research employs the CCM (convergent cross-mapping) method to understand the influence of individual meteorological factors on local PM 2.5 concentrations in 188 monitoring cities across China. Results indicate that meteorological influences on PM 2.5 concentrations have notable seasonal and regional variations. For the heavily polluted North China region, when PM 2.5 concentrations are high, meteorological influences on PM 2.5 concentrations are strong. The dominant meteorological influence for PM 2.5 concentrations varies across locations and demonstrates regional similarities. For the most polluted winter, the dominant meteorological driver for local PM 2.5 concentrations is mainly the wind within the North China region, whilst precipitation is the dominant meteorological influence for most coastal regions. At the national scale, the influence of temperature, humidity and wind on PM 2.5 concentrations is much larger than that of other meteorological factors. Amongst eight factors, temperature exerts the strongest and most stable influence on national PM 2.5 concentrations in all seasons. Due to notable temporal and spatial differences in meteorological influences on local PM 2.5 concentrations, this research suggests pertinent environmental projects for air quality improvement should be designed accordingly for specific regions.

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Section: Introductionmentioning

confidence: 99%

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

et al. 2018

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“…Additionally, concentrations of large fractions were also reduced when the ground surface was covered with snow or ice. Again based on GAM analysis, Pearce et al (2011) concluded that local-scale meteorological conditions have the largest impact on air quality in Melbourne, Australia. The most significant variables for PM 10 were found to be temperature, wind, water vapour pressure and boundary layer height, with temperature, followed by wind and water vapour pressure, substantially affecting NO 2 .…”

Section: )mentioning

confidence: 99%

“…during low pressure events, the atmosphere is unstable and pollutants can be transported by vertical fluxes, which are thus associated with pollution dilution (e.g. Prtenjak et al, 2009;Pearce et al, 2011). However, as illustrated in Fig.…”

Section: Partial Effectsmentioning

confidence: 99%

“…As statistical models able to simulate nonlinear relationships by smoothing concurrent input variables (Hastie and Tibshirani, 1990;Hastie et al, 2009), GAMs are suitable for a wide range of environmental issues and have thus been used in many recent studies (e.g. Schlink et al, 2003;Aldrin and Haff, 2005;Ito et al, 2007;Zhang and Batterman, 2010;Pearce et al, 2011;Chen et al, 2012;Gonzales et al, 2012;Munir et al, 2013;quisthoudt, 2013;Otto et al, 2014;Wanka et al, 2014). According to Schlink et al (2003), GAMs performance is comparable to those of neural network models, and, GAMs are applicable for ozone forecasting, for which purpose they (as well as the neural network approach) outperform linear forecasting techniques.…”

Section: )mentioning

confidence: 99%

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Using a generalized additive model to quantify the influence of local meteorology on air quality in Zagreb

2015

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This paper reports the estimated response of hourly mean concentrations of selected air pollutants, namely carbon monoxide (CO), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ) and particulate matter with an aerodynamic diameter of up to 10 µm (PM 10 ), to local scale meteorology in Zagreb, Croatia for the period 2006-2012. A new method is applied here for the urban area of Zagreb. In a general model, a logarithm of hourly mean air pollutant concentrations is expressed as the sum of the nonlinear functions of meteorological and several time variables, with the latter included accounting for temporal variation in emissions. The model can be formulated within the framework of generalized additive models (GAMs) and is additive on the logarithmic scale, which results in multiplicative effects on the original scale. Although the model is nonlinear, it is simple and easy to interpret. It quantifies the impact of meteorological conditions and emissions on air pollution. A measure of the relative importance of each predictor, partial effects and statistical evaluation of the model are also presented. Overall, the results show that the most important predictors are those related to emissions. The aggregate impact of meteorological variables in the model explained 45% of variance in CO, 14% in SO 2 , 25% in NO 2 and 24% in PM 10 . This indicates that meteorology, at least on a local scale, is a noticeable driver of air quality in Zagreb. Stable atmospheric conditions in the urban area favour the occurrence of higher concentrations of air pollutants. Convection processes dominate under unstable conditions, resulting in the dilution of pollutant concentrations within the boundary layer.

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“…The Caribbean study area (Figure 1a) [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] • N, 79-54 • W extends from Trinidad to Cuba. Current information on country emissions and sector burdens were drawn from the Food and Agriculture Organization [48] database, and fire emission data were obtained from the Global Fire Emissions Database (GFED) [49].…”

Section: Datamentioning

confidence: 99%

Caribbean Air Chemistry and Dispersion Conditions

Jury

2017

Atmosphere

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Abstract:The meteorological influences on Caribbean air chemistry are studied using in-situ, satellite and model data. Although African dust plumes join locally generated pollutants, concentrations are relatively low in the eastern Caribbean due to geographic remoteness and steady oceanic trade winds. Urban-industrial emissions from big cities (e.g., Kingston, Santo Domingo, San Juan), agricultural emissions from the south, and volcanic emissions from Montserrat contribute a noticeable burden. Conditions over Puerto Rico in the dry season (December-May) provide a focus for statistical analysis of air chemistry constituents and weather variables that describe dispersion conditions. Monthly and daily air indices are formed by summing the normalized values of fine aerosols and particulates, long-and short-lived trace gases from in-situ, satellite and model sources. The spatial correlation of a daily Puerto Rico air index onto regional dewpoint temperature, air pressure and outgoing longwave radiation fields in December-May 2005-2015 reveals the northward movement of a dry tongue and trough. At the climate timescale, Pacific El Nino conditions favor an increase of spring-time air pollution corresponding to anomalous inflow from Africa and the southern Caribbean coast. Composite weather patterns for a group of high air index values reflect divergent trade winds and a strong jet stream that imparts anticyclonic vorticity, subsidence and low humidity. This new understanding will underpin better air quality forecasts for Puerto Rico and the wider Caribbean.

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Quantifying the influence of local meteorology on air quality using generalized additive models

Cited by 266 publications

References 23 publications

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Using a generalized additive model to quantify the influence of local meteorology on air quality in Zagreb

Caribbean Air Chemistry and Dispersion Conditions

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Quantifying the influence of local meteorology on air quality using generalized additive models

Cited by 266 publications

References 23 publications

Understanding meteorological influences on PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations across China: a temporal and spatial perspective

Using a generalized additive model to quantify the influence of local meteorology on air quality in Zagreb

Caribbean Air Chemistry and Dispersion Conditions

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Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective