Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5

Estimación de la concentración media diaria de material particulado fino en la región del Complejo Industrial y Portuario de Pecém, Ceará, Brasil

A exposição ao material particulado fino (MP2,5) está associada a inúmeros desfechos à saúde. Desta forma, monitoramento da concentração ambiental do MP2,5 é importante, especialmente em áreas amplamente industrializadas, pois abrigam potenciais emissores do MP2,5 e de substâncias com potencial de aumentar a toxicidade de partículas já suspensas. O objetivo desta pesquisa é estimar a concentração diária do MP2,5 em três áreas de influência do Complexo Industrial e Portuário do Pecém (CIPP), Ceará, Brasil. Foi aplicado um modelo de regressão não linear para a estimativa do MP2,5, por meio de dados de profundidade óptica monitorados por satélite. As estimativas foram realizadas em três áreas de influência (Ai) do CIPP (São Gonçalo do Amarante – Ai I, Paracuru e Paraipaba – Ai II e Caucaia – Ai III, no período de 2006 a 2017. As médias anuais das concentrações estimadas foram inferiores ao estabelecido pela legislação nacional em todas as Ai (8µg m-3). Em todas as Ai, os meses referentes ao período de seca (setembro a fevereiro) apresentaram as maiores concentrações e uma predominância de ventos leste para oeste. Os meses que compreendem o período de chuva (março a agosto) apresentaram as menores concentrações e ventos menos definidos. As condições meteorológicas podem exercer um papel importante nos processos de remoção, dispersão ou manutenção das concentrações do material particulado na região. Mesmo com baixas concentrações estimadas, é importante avaliar a constituição das partículas finas dessa região, bem como sua possível associação a efeitos adversos à saúde da população local.Exposure to fine particulate matter (PM2.5) is associated with numerous negative health outcomes. Thus, monitoring the environmental concentration of PM2.5 is important, especially in heavily industrialized areas, since they harbor potential emitters of PM2.5 and substances with the potential to increase the toxicity of already suspended particles. This study aims to estimate daily concentrations of PM2.5 in three areas under the influence of the Industrial and Port Complex of Pecém (CIPP), Ceará State, Brazil. A nonlinear regression model was applied to estimate PM2.5, using satellitemonitored optical depth data. Estimates were performed in three areas of influence (Ai) of the CIPP (São Gonçalo do Amarante – AiI, Paracuru and Paraipaba – AiII, and Caucaia – AiIII), from 2006 to 2017. Estimated mean annual concentrations were lower than established by Brazil’s national legislation in all three Ai (8µg m-³). In all the Ai, the months of the dry season (September to February) showed the highest concentrations and a predominance of east winds, while the months of the rainy season (March to August) showed the lowest concentrations and less defined winds Weather conditions can play an important role in the removal, dispersal, or maintenance of concentrations of particulate matter in the region. Even at low estimated concentrations, it is important to assess the composition of fine participles in this region and their possible association with adverse health outcomes in the local population.La exposición al material particulado fino (MP2,5) está asociada a innumerables problemas de salud. Por ello, la supervisión de la concentración ambiental del MP2,5 es importante, especialmente en áreas ampliamente industrializadas, puesto que albergan potenciales emisores de MP2,5 y de sustancias con potencial de aumentar la toxicidad de partículas ya suspendidas. El objetivo de esta investigación es estimar la concentración diaria del MP2,5 en tres áreas de influencia del Complejo Industrial y Portuario de Pecém (CIPP), Ceará, Brasil. Se aplicó un modelo de regresión no lineal para la estimación del MP2,5, mediante datos de profundidad óptica supervisados por satélite. Las estimaciones fueron realizadas en tres áreas de influencia (Ai) del CIPP (São Gonçalo do Amarante – Ai I, Paracuru y Paraipaba – Ai II y Caucaia – Ai III en el período de 2006 a 2017. Las medias anuales de las concentraciones estimadas fueron inferiores a lo establecido por la legislación nacional en todas las Ai (8µg m-³). En todas las Ai, los meses referentes al período de sequía (de setiembre a febrero) presentaron las mayores concentraciones y una predominancia de vientos este a oeste, los meses que comprenden el período de lluvia (marzo a agosto) presentaron las menores concentraciones y vientos menos definidos. Las condiciones meteorológicas pueden ejercer un papel importante en los procesos de eliminación, dispersión o mantenimiento de las concentraciones del material particulado en la región. Incluso con bajas concentraciones estimadas es importante que se evalúe la constitución de las partículas finas de esta región, así como su posible asociación con efectos adversos para la salud de la población local

Reduced ultrafine particle levels in São Paulo’s atmosphere during shifts from gasoline to ethanol use

International audienceDespite ethanol's penetration into urban transportation, observational evidence quantifying the consequence for the atmospheric particulate burden during actual, not hypothetical, fuel-fleet shifts, has been lacking. Here we analyze aerosol, meteorological, traffic, and consumer behavior data and find, empirically, that ambient number concentrations of 7–100-nm diameter particles rise by one-third during the morning commute when higher ethanol prices induce 2 million drivers in the real-world megacity of São Paulo to substitute to gasoline use (95% confidence intervals: +4,154 to +13,272 cm −3). Similarly, concentrations fall when consumers return to ethanol. Changes in larger particle concentrations, including US-regulated PM2.5, are statistically indistinguishable from zero. The prospect of increased biofuel use and mounting evidence on ultrafines' health effects make our result acutely policy relevant, to be weighed against possible ozone increases. The finding motivates further studies in real-world environments. We innovate in using econometrics to quantify a key source of urban ultrafine particles