Urban background air quality stations are representative of the city-wide pollution and are used (i) for regulatory purposes and (ii) to estimate the average exposure for the population. However, just one or a few urban sites are usually located within major cities in routine monitoring networks. This sparse spatial coverage is insufficient to capture the spatial variability expected in urban areas. This serious gap causes problems when performing human exposure studies: poor spatial resolution may mask the exposure variability in the study population and may lead to potential exposure misclassifications that reduces the power of the study. Recent advance in micro-scale technology have made available low-cost sensors now that permit continuous and simultaneous measurements to be made in multiple locations. However, low-cost monitors do not meet rigid performance standards and produce data that need to be carefully evaluated and unbiased before being used for scientific purposes [1]. Measurements of ambient PM concentrations were performed using 25 low-cost monitors from October to April 2015–2016 and 2016–2017 to assess the spatial and temporal variability in PM and the relative importance of traffic and wood smoke to outdoor PM concentrations in Rochester, NY, USA [2]. In general, results showed a moderate spatial inhomogeneity. Pearson correlation coefficients were often moderate (~50% of units showed correlations >0.5 during the first season), indicating that there was some coherent variation across the urban area. Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. LUR models based on the deletion/substitution/addition algorithm were built from the low-cost monitor data for each hour of the day and weekdays/weekend days. Coefficients of determination for hourly PM predictions ranged from 0.63 to 0.67. The technology behind the current commercially available low-cost monitor still needs substantial improvements to return devices able to approximate PM concentrations similarly to conventional scientific-grade instruments. However, our results also show that the use of this new technology may be useful in increasing the spatial resolution of regulatory air quality networks. Bibliografia [1] N. Zikova et al. (2017). J. Aerosol Sciences, 105, 24–34. [2] N. Zikova et al. (2017). Sensors, 17, 1922

Estimation of the intra-urban variability of particulate matter concentrations using low-cost monitors and land use regression models

MASIOL M.;
2018

Abstract

Urban background air quality stations are representative of the city-wide pollution and are used (i) for regulatory purposes and (ii) to estimate the average exposure for the population. However, just one or a few urban sites are usually located within major cities in routine monitoring networks. This sparse spatial coverage is insufficient to capture the spatial variability expected in urban areas. This serious gap causes problems when performing human exposure studies: poor spatial resolution may mask the exposure variability in the study population and may lead to potential exposure misclassifications that reduces the power of the study. Recent advance in micro-scale technology have made available low-cost sensors now that permit continuous and simultaneous measurements to be made in multiple locations. However, low-cost monitors do not meet rigid performance standards and produce data that need to be carefully evaluated and unbiased before being used for scientific purposes [1]. Measurements of ambient PM concentrations were performed using 25 low-cost monitors from October to April 2015–2016 and 2016–2017 to assess the spatial and temporal variability in PM and the relative importance of traffic and wood smoke to outdoor PM concentrations in Rochester, NY, USA [2]. In general, results showed a moderate spatial inhomogeneity. Pearson correlation coefficients were often moderate (~50% of units showed correlations >0.5 during the first season), indicating that there was some coherent variation across the urban area. Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. LUR models based on the deletion/substitution/addition algorithm were built from the low-cost monitor data for each hour of the day and weekdays/weekend days. Coefficients of determination for hourly PM predictions ranged from 0.63 to 0.67. The technology behind the current commercially available low-cost monitor still needs substantial improvements to return devices able to approximate PM concentrations similarly to conventional scientific-grade instruments. However, our results also show that the use of this new technology may be useful in increasing the spatial resolution of regulatory air quality networks. Bibliografia [1] N. Zikova et al. (2017). J. Aerosol Sciences, 105, 24–34. [2] N. Zikova et al. (2017). Sensors, 17, 1922
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3724626
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