Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy

Cattani, Giorgio; Gaeta, Alessandra; Bucchianico, A. Di; Santis, Antonella De; Gaddi, Raffaela; Cusano, Mariacarmela; Ancona, Carla; Badaloní, Chiara; Forastiére, Francesco; Gariazzo, Claudio; Sozzi, Roberto; Inglessis, Marco; Silibello, Camillo; Salvatori, Elisabetta; Manes, Fausto; Cesaroni, Giulia

doi:10.1016/j.atmosenv.2017.02.028

Cited by 41 publications

(25 citation statements)

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“…Usually, most of the predictors are geographical variables (such as roads), traffic, meteorological data, and others [13]. LUR models are usually applied in small cities or regions where AQMN stations are limited [51], and where spatial interpolation techniques, such as ordinary kriging or inverse distance weighting,…”

Section: Discussionmentioning

confidence: 99%

“…LUR models are usually applied in small cities or regions where AQMN stations are limited [51], and where spatial interpolation techniques, such as ordinary kriging or inverse distance weighting, cannot be applied, considering the low number of ground measurements available [52]. One of the main problems with these geographic variables is the low accessibility to the data and the time of acquisition.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

et al. 2019

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The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

et al. 2019

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“…For each Île-de-France department, eight SIREN simulations are conducted (five for dwellings and three for offices/schools) at a 3 min time step. The penetration factor is fixed to 0.8 through the building shell and 1 through air inlet based on the state of the art (Chen and Zhao, 2011;Monn, 2001;Stephens et al, 2012;Thatcher et al, 2003). Confronting numerical simulations with SIREN and I / O ratio measurements, the deposition rate was fixed to 0.1 h −1 .…”

Section: Dwellings Offices and Schoolsmentioning

confidence: 99%

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>

et al. 2020

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Abstract. This paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate (1) space–time activity event sequences, (2) the infiltration of atmospheric contaminants indoors, and (3) daily aggregated personal exposure. The model is applied over the greater Paris region at 2 km×2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11 % (population average). Moreover, accounting for PM2.5 exposure during transportation (in vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5 %. We show that the spatial distribution of PM2.5 and O3 exposure is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. Overall, 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg m−3), compared to 0 % for people travelling by car. Finally, we develop a 2050 horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposure. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5.

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“…Recent studies have reported adverse health effects of PM 2.5 exposure in Rome. 2,9,31,32,34 To better understand and quantify the health impacts of PM 2.5 on Rome City residents, the latest version of WHO recommended AirQ + modeling system (v3.0) was used in order to estimate the number of excess mortality cases of IHD, COPD, LC, stroke and WDL (morbidity) from long-term PM 2.5 exposure in Rome. In this study, ambient PM 2.5 concentrations were acquired from 1st January to 31 st December 2014 from 4 monitoring urban stations, representative of the background levels.…”

Section: Discussionmentioning

confidence: 99%

“…30 Rome is one of the European megacities with more than 3 million inhabitants; the city has experienced high PM 2.5 emissions over the years mostly due to high road traffic, biomass burning and industrial activities. 2,31 In Rome, PM 2.5 are emitted, in particular in early winter season, and are composed of carbonaceous matter, K and Na, characteristics of road traffic, heating and sea-salt. 32 The MED-PARTICLES project published results from a multi centric study involving 10 European cities.…”

Section: Introductionmentioning

confidence: 99%

Long-term exposure to ambient PM2.5 and impacts on health in Rome, Italy

Amoatey

Sicard

Marco

et al. 2020

Clinical Epidemiology and Global Health

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The aim of this study was to estimate the impact of long-term exposure to PM 2.5 on residents of Rome, Italy in terms of ischemic heart diseases (IHD), chronic obstructive pulmonary diseases (COPD), lung cancer (LC), stroke and the number of working days lost (WDL). Methods: In this study, we estimated human health impacts from long-term exposure to ambient PM 2.5 through application of linear RR and integrated exposure-response (IER) functions and the AirQ + software. Results: In 2014, on average 1189, 348, 43, 301 and 387 cases of IHD, COPD, LC, stroke and WDL, respectively could be avoided in Rome if the annual mean PM 2.5 concentration was reduced from 15.6 to 10.0 μg m −3. In 2014, 27.67% of IHD, 15.9% of COPD, 9.5% of LC, 19.9% of stroke as well as 2.5% of WDL are attributed to the long-term exposure to PM 2.5 concentrations exceeding 10 μg m −3. Conclusion: This may be achieved through adoption of stringent air pollution regulations and sustainable city planning. Increase in urban green infrastructures and improving road transportation will reduce PM 2.5 levels in urban environment, thereby safeguarding human health from air pollution and improving citizens' well-being.

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Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy

Cited by 41 publications

References 50 publications

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>

Long-term exposure to ambient PM2.5 and impacts on health in Rome, Italy

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Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy

Cited by 41 publications

References 50 publications

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

EXPLUME v1.0: a model for personal exposure to ambient O&lt;sub&gt;3&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt;

Long-term exposure to ambient PM2.5 and impacts on health in Rome, Italy

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EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>