Correlation between solar radiation and total syspended particulate matter in Santiago, Chile?Preliminary results

Préndez, Margarita; Egido, J.L.; Tomás, C.; Seco, Joao; Calvo, Ana; Romero, Hugo

doi:10.1016/1352-2310(94)00349-p

Cited by 27 publications

(9 citation statements)

References 6 publications

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“…It can be clearly seen from the Table 3 that correlations of SO 2 and TSP with temperature, wind speed and, relative humidity obtained at this study is different from those of found at other studies (Prendez et al 1995;Gupta et al 2003). Note: All meteorological parameters were measured at ground level The regression analysis…”

Section: Relationship Between So 2 Tsp and Meteorological Factorscontrasting

confidence: 54%

Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey

İlten

Selici

2007

Environ Monit Assess

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Air pollution is one of the most important environmental problems in Balikesir, situated in the western part of Turkey, during the winter periods. The unfavorable climate as well as the city's topography, and inappropriate fuel usage cause serious air pollution problems. The air pollutant concentrations in the city have a close relationship with meteorological parameters. In the present study, the relationship between daily average total suspended particulate (TSP) and sulphur dioxide (SO2) concentrations measured between 1999--2005 winter seasons were correlated with meteorological factors, such as wind speed, temperature, relative humidity and pressure. This statistical analysis was achieved using the stepwise multiple linear regression method. According to the results obtained through the analysis, higher TSP and SO2 concentrations are strongly related to colder temperatures, lower wind speed, higher atmospheric pressure and higher relative humidity. The statistical models of SO2 and TSP gave correlation coefficient values (R2) of 0.735 and 0.656, respectively.

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Section: Relationship Between So 2 Tsp and Meteorological Factorscontrasting

confidence: 54%

Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey

İlten

Selici

2007

Environ Monit Assess

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“…The residence of pollutants in the atmosphere and the development of secondary pollutants are controlled not only by the pace of release of the reactants into the air from the source but also by the source conditions as well as meteorological parameters (temperature, wind speed, wind direction, relative humidity, rainfall, solar radiation, etc.) (Bhaskar and Mehta 2010;Deshmukh et al 2013a;Prendez et al 1995). A dilution effect is also produced on the pollution concentration by the approaching wind.…”

Section: Influence Of Meteorological Factors On Particulate Mattermentioning

confidence: 96%

The effect of climate and meteorological changes on particulate matter in Pune, India

Yadav

Praveen

Satsangi

2015

Environ Monit Assess

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This paper presents the distinctiveness of particulate matter (PM) mass concentrations (PM10, PM2.5, and PM10-2.5) and meteorological effect in Pune city during 2011-2012. The PM samples were collected using Mini-Vol TAS air sampler (Airmetrics Co. Inc., 5 l min(-1) flow rate). The meteorological parameters were also measured during the study period. The analysis of 24-h average PM10, PM2.5, and PM10-2.5 concentrations showed the maximum during winter (267.2-67.2, 180.6-55.6, 138.9-11.7 μg m(-3)) followed by summer (236.1-55.5, 138.8-27.7, 125-13.8 μg m(-3)) and post-monsoon (153.3-82.3, 138.9-41.7, 41.7-14.4 μg m(-3)) and showed the lowest concentration during monsoon (98.9-27.8, 83.3-13.9, 40.0-6.0 μg m(-3)) seasons in the entire study. PM10 comprised a vast fraction of PM2.5 (61% of PM2.5), while the estimated PM2.5/PM10 ratios for monsoon, post-monsoon, winter, and summer seasons were ranged between 0.5 and 0.9, 0.51 and 0.91, 0.3 and 0.9, and 0.3 and 0.8, respectively. The 7-day back trajectories analysis for whole year shows that the air masses transported to Pune were mixed mainland-maritime such as from southwesterly, north, northwest. Chemometric analysis was applied as a tool to evaluate and predict the particulate mass concentration from available meteorological data. To achieve this, a calibration model was developed by partial least squares regression (PLSR) method and was further used to predict the PM concentrations based on meteorological data. On predicting the PM concentration from local meteorological data, the model performance and quality was found very good in case of PM10 compared to PM2.5.

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“…Some meteorological variables indicating the atmospheric conditions also contributed to the estimation of ground-level PM concentrations in the improved models. There is a relationship between solar radiation and aerosols in which solar radiation reaching the surface increases with decreasing aerosol concentration (Préndez et al, 1995;Hu et al, 2017a;Borlina and Rennó, 2017). Prior studies noted that there is an inverse relationship between wind speed and both PM 10 and PM 2.5 (Gupta et al, 2006;Maraziotis et al, 2008;Krynicka and Drzeniecka-Osiadacz, 2013).…”

Section: Performance Of the Rf Modelsmentioning

confidence: 98%

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Park

Shin

et al. 2019

Atmos. Chem. Phys.

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Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

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Correlation between solar radiation and total syspended particulate matter in Santiago, Chile?Preliminary results

Cited by 27 publications

References 6 publications

Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey

Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey

The effect of climate and meteorological changes on particulate matter in Pune, India

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

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