Factors affecting levels of aerosol sulfate and nitrate on the Western Mediterranean coast

Galindo, Nuria; Nicolás, J.F.; Yubero, Eduardo; Caballero, Sandra; Pastor, C.; Crespo, J.

doi:10.1016/j.atmosres.2007.11.024

Cited by 46 publications

(27 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The difference in effects was explained by human activity patterns, which change depending on the season. Liu et al (2012) also suggested that the difference of the effects of pollutants should be taken into consideration when seeking to model mortality data explained by air pollution, which corroborates the results of Makie et al (2002);Hagler et al (2006); Galindo et al (2008) and Viana et al (2014). Both these studies found that where the pollutants' composition is known, it varies in the spatial domain; which suggests that seasonal patterns should be examined by geographic region.…”

Section: Discussionsupporting

confidence: 73%

Dynamic Regression Model for Evaluating the Association Between Atmospheric Conditions and Deaths due to Respiratory Diseases in São Paulo, Brazil

Gomes

Spyrides

Lúcio

2018

Rev. bras. meteorol.

View full text Add to dashboard Cite

The article reports the modeling of mortality due to respiratory diseases emanating from atmospheric conditions, capturing significant associations and verifying the ability of stochastic modeling to predict deaths arising from the relationship between weather conditions and air pollution. The statistical methods used in the analysis were cross-correlation and pre-whitening, in addition to dynamic regression modeling combining the dynamics of time series and the effect of explanatory variables. The results show there are significant associations between mortality and sulfur dioxide, air temperature, atmospheric pressure, relative humidity, and autoregressive structure. The cross-correlations captured significant lags between atmospheric variables and deaths, of two months for SO 2 and relative humidity, eleven months for PM 10 , seven months for O 3 , and eight months for air temperature and the cross-correlation without lag with NO 2 . With CO variables, precipitation and atmospheric pressure, cross-correlations were not detected. Stochastic modeling showed that deaths due to respiratory diseases can be predicted from the combination of meteorological and air pollution variables, especially considering the existing trend and seasonality.Keywords: forecasting, mortality, meteorology, air pollution. Modelo Dinâmico de Regressão Para Avaliar a Associação Entre as Condições Atmosféricas e as Mortes por Doenças Respiratórias em São Paulo, Brasil ResumoEsse estudo objetivou modelar a mortalidade por doenças respiratórias explicadas por condições atmosféricas, com o intuito de identificar associações significativas e verificar a capacidade da modelagem estocástica em prever óbitos decorrentes da relação entre condições meteorológicas e poluição do ar. Os métodos estatísticos utilizados na análise foram: correlação cruzada, pré-branqueamento e o modelo de regressão dinâmica que combina a dinâmica das séries de tempo e o efeito de variáveis explicativas. Os resultados mostram que existem associações significativas ao nível de 5% entre mortalidade e dióxido de enxofre, temperatura do ar, pressão atmosférica, umidade relativa e a estrutura auto-regressiva. As correlações cruzadas identificaram defasagens significativas entre as variáveis atmosféricas e o número de óbitos, em dois meses para o SO 2 e umidade relativa, onze meses para PM 10 , sete meses para o O 3 , oito meses para a temperatura do ar e a correlação cruzada sem defasagem com o NO 2 . Com as variáveis CO, precipitação e pressão atmosférica, a correlação cruzada não foi detectada. A modelagem estocástica mostrou que o número de óbitos por doenças respiratórias pode ser previsto a partir da combinação das variáveis meteorológicas e os poluentes atmosféricos, especialmente considerando a tendência e sazonalidade existente.Palavras-chave: previsão, mortalidade, meteorologia, poluição do ar.Revista Brasileira de Meteorologia, v. 33, n.

show abstract

Section: Discussionsupporting

confidence: 73%

Dynamic Regression Model for Evaluating the Association Between Atmospheric Conditions and Deaths due to Respiratory Diseases in São Paulo, Brazil

Gomes

Spyrides

Lúcio

2018

Rev. bras. meteorol.

View full text Add to dashboard Cite

show abstract

“…Large quantities of NH 4 + and SO 4 −2 characterize the fifth source and clearly represent the presence of ammonium sulfate in the area (Nicolás et al 2009b;Galindo et al 2008) from photooxidation of sulfuric oxides initially emitted as gases from local emissions (for example the cement plant) and long-distant transport. The NH 4 + presence is not large enough to neutralize the entire existing sulfate in the atmosphere.…”

Section: Resultsmentioning

confidence: 99%

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

Yubero

Carratalá

Crespo

et al. 2010

Environ Sci Pollut Res

View full text Add to dashboard Cite

Introduction The concentrations of trace metals, ionic species, and carbonaceous components in PM 10 (particulate matter with aerodynamic diameters smaller than 10 µm) were measured from samples collected near an industrial complex, primarily composed of cement plants, in southeastern Spain, from September 2005 to August 2006. Materials and methods Positive matrix factorization and conditional probability function were applied to this data set to identify different types of sources. Results Six significant sources were identified: crustal matter, traffic, aged sea salt, industrial emissions, secondary aerosol, and sea salt. The difficulty of separating anthropogenic sources from those of natural origin is highlighted in this study; in particular, the crustal source can be connected with both natural (African outbreaks, wind resuspension) and man-made emissions, like fugitive emissions in an industrial environment.

show abstract

“…In contrast, there is no such heating period in southern China. Third, the composition of particulate matter is known to vary in the spatial domain as well, which suggests that seasonal patterns should be examined by geographic region (Hagler et al 2006;Galindo et al 2008).…”

Section: Discussionmentioning

confidence: 99%

Seasonal effects of PM10 concentrations on mortality in Tianjin, China: a time-series analysis

Zhou

Zhang

et al. 2012

J Public Health

View full text Add to dashboard Cite

Aim There are uncertainties regarding the modification effects of season and temperature on the relationship between air pollution and mortality. This study aims to examine the effects of season and temperature on mortality associated with particulate matter less than 10 μm in diameter (PM 10 ) in Tianjin, China. Methods Time-series analysis was used to explore the modification effects of season and temperature on the association between PM 10 and cause-specific mortality in Tianjin between 2006 and 2009. The causes studied were overall nonaccidental mortality and subcategories of cardiovascular, respiratory, cardiopulmonary, stroke, and ischemic heart diseases (IHD). Results The association between PM 10 and mortality showed a strong seasonal pattern and the effects of PM 10 on causespecific mortalities were strongest for high temperature days. Generally, the adverse effects were stronger in summer than for other seasons, except for respiratory mortality. In the summer period, a 10 μg/m 3 increase in PM 10 at 0-1 days was associated with an increase in mortality for nonaccidental (0.95 %, 95 % confidence interval [CI]: 0.45, 1.45), cardiovascular (1.40 %, 95 %CI: 0.77, 2.03), cardiopulmonary (1.37 %, 95 %CI: 0.77, 1.98), IHD (1.55 %, 95 %CI: 0.73, 2.37), and stroke (1.27 %, 95 % CI: 0.37, 2.12) causes. The overall increase in mortality per 10 μg/m 3 increase in PM 10 was 0.42 % (95 %CI: 0.26, 0.58) for nonaccidental, 0.41 % (95 %CI: 0.21, 0.62) for cardiovascular, 0.46 % (95 %CI: 0.26,0.65) for cardiopulmonary, 0.57 % (95 %CI: 0.30, 0.84) for IHD, and 0.32 % (95 %CI: 0.03,0.61) for stroke. At high temperatures (≥23.8°C), a 10 μg/m 3 increase in PM 10 at 0-1 days was associated with an increase in mortality of 0.90 % (95 %CI: 0.61,1.19) for non-accidental, 1.01 % for cardiovascular, 1.40 % (95 %CI: 0.55, 2.27) for respiratory, 1.06 % (95 %CI: 0.71, 1.41) for cardiopulmonary, 1.47 % (95 %CI: 1.00, 1.94) for IHD, and 0.75 % (95 %CI: 0.24, 1.28) for stroke. In addition, the PM 10 effects of high temperature days were stronger for those aged 65 years and over. Conclusion Season and temperature could modify the adverse effects of PM 10 . An increase in hot summer days caused by climate change may enhance the risks of air pollution on human health. More attention should be paid to older populations, especially in summer and days with high temperatures.

show abstract

Factors affecting levels of aerosol sulfate and nitrate on the Western Mediterranean coast

Cited by 46 publications

References 25 publications

Dynamic Regression Model for Evaluating the Association Between Atmospheric Conditions and Deaths due to Respiratory Diseases in São Paulo, Brazil

Dynamic Regression Model for Evaluating the Association Between Atmospheric Conditions and Deaths due to Respiratory Diseases in São Paulo, Brazil

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

Seasonal effects of PM10 concentrations on mortality in Tianjin, China: a time-series analysis

Contact Info

Product

Resources

About