PM_2.5 mass and species trends in Santiago, Chile, 1998 to 2010: The impact of fuel-related interventions and fuel sales

Abstract

Improving air quality in Santiago has been a high priority for the Chilean government. In this paper, we examine trends of fine particulate matter (PM 2.5 ) mass and species concentrations during the period 1998 to 2010 and explore the impact of fuel-related interventions and fuel sales on concentration changes. Smoothing spline functions were utilized to characterize and account for nonlinear relationships between pollutant concentrations and different parameters. Meteorology-adjusted PM 2.5 concentrations were lower by 21.8 µg/m 3 in 2010 compared to 1998. In this model, wind speed was the most important determinant of PM 2.5 levels. A decrease in 24-hr average wind speed below 1.0 m/s was associated with a significant increase in daily PM 2.5 levels, indicating a high sensitivity of PM 2.5 concentrations to the accumulation of local emissions. The same regression model framework was applied to examine the trends of lead, bromine, and sulfur concentrations. Removal of lead and bromine from gasoline achieved dramatic decreases in their atmospheric concentrations. Nonetheless, both elements continue to persist, likely in the form of PbBrCl. The reduction of diesel sulfur content from 1,500 to 50 ppm corresponded to a 32% decrease in particulate sulfur levels. Lastly, a surge in PM 2.5 was observed in [2005][2006][2007][2008]. Further regression analyses suggested this was prompted by a rise in monthly petroleum-based fuel sales.Implications: In this paper, we elucidate meteorology-adjusted trends of PM 2.5 mass and species concentrations in Santiago and assess the efficacy of fuel-related interventions, such as the removal of lead from gasoline and reduction of sulfur content in diesel. In addition, we explore the impact of fuel sales on PM 2.5 trends. Given that fuel consumption is likely to increase further in this rapidly growing city, understanding its impact on PM 2.5 trends can inform future air quality control efforts in Santiago.

Characteristics of PM2.5 concentrations across Beijing during 2013–2015

“…There were 2 to 5 air pollution episodes each month (at least two consecutive days when the Beijing average concentration exceeded 75 µg·m −3 ). PM 2.5 levels tended to be lowest in summer (particularly in August), and highest in winter, a pattern frequently observed elsewhere (Dimitriou and Kassomenos, 2014; Jhun et al, 2013; M.Marcazzan et al, 2001; Yang, 2002). Winters in Beijing are associated with conditions that prevailed in the “classical” air pollution episodes of the earthly and mid-20 th century, namely, high emissions of particulate and gaseous pollutants from coal consumption for heating and industry, and frequent stagnation conditions due to subsidence inversions that impede pollutant dilution and transport (Hu et al, 2013; Shi et al, 2003; Zhao et al, 2009).…”

The impact of nitrogen oxides concentration decreases on ozone trends in the USA

“…Here, we applied generalized additive models (GAMs) (Hastie and Tibshirani 1990) to adjust for the nonlinear relationship between O 3 and NO x . GAMs have been applied extensively for both meteorological and nonmeteorological adjustment in air pollution trend analyses (Barmpadimos et al 2012; Zheng et al 2007; Jhun et al 2013). Trends of O 3 , NO x -adjusted O 3 , and NO x were calculated for each region, season, and time of day from the following GAMs, using the statistical package R (http://www.r-project.org): $${false({\mathrm{O}}_{3}false)}_{\text{ij}}={\beta }_0+{\beta }_{1,\text{actual}}{\text{year}}_{\text{ij}}+\gamma {\text{month}}_{\text{ij}}+\delta {\text{weekday}}_{\text{ij}}+{\epsilon }_{\text{ij}}$$ $${false({\mathrm{O}}_{3}false)}_{\text{ij}}={\beta }_0+{\beta }_{1,\text{adjusted}}{\text{year}}_{\text{ij}}+\gamma {\text{month}}_{\text{ij}}+\delta {\text{weekday}}_{\text{ij}}+s\left({({\text{NO}}_{\mathrm{normalx}})}_{\text{ij}}\right)+{\epsilon }_{\text{ij}}$$ $${false({\text{NO}}_{\mathrm{x}}false)}_{\text{ij}}={\beta }_0+{\beta }_{1,\text{NOx}}{\text{year}}_{\text{ij}}+\gamma {\text{month}}_{\text{ij}}+\delta {\text{weekday}}_{\text{ij}}+{\epsilon }_{\text{ij}}$$ where, (O 3 ) ij and (NO x ) ij represent O 3 and NO x concentrations, respectively, at site i and on date j and; β 1,actual , β 1,adjusted , and β 1,NOx estimate regional trends of O 3 , NO x -adjusted O 3 , and NO x , respectively, for a given season and hour of the day.…”

Fine and Coarse Aerosol at Rio de Janeiro prior to the Olympic Games: Chemical Composition and Source Apportionment

“…This finding may reflect that larger and heavier vehicular traffic is associated with lower sulfur content in diesel fuel (from S-500 to S-50 during 2012 and S-10 during 2013). In Santiago (Chile), Jhun et al 19 observed a 32% decrease in particulate sulfate as a consequence of the diesel sulfur reduction from 1500 to 50 mg kg -1 . Comparing the results obtained by Andrade et al 18 and the current work for the same sampling region (Duque de Caxias), several parameters differed by a factor of two.…”