This work originates from an epidemiological study aimed to assess the correlation between population exposure to pesticides used in agriculture and adverse health effects. In support of the population exposure evaluation two models implemented by the authors were applied: a GIS-based proximity model and the CAREA atmospheric dispersion model. In this work, the results of the two models are presented and compared. Despite the proximity analysis is widely used for these kinds of studies, it was investigated how meteorology could affect the exposure assessment. Both models were applied to pesticides emitted by 1519 agricultural fields and considering 2584 receptors distributed over an area of 8430 km2. CAREA output shows a considerable enhancement in the percentage of exposed receptors, from the 4% of the proximity model to the 54% of the CAREA model. Moreover, the spatial analysis of the results on a specific test site showed that the effects of meteorology considered by CAREA led to an anisotropic exposure distribution that differs considerably from the symmetric distribution resulting by the proximity model. In addition, the results of a field campaign for the definition and planning of ground measurement of concentration for the validation of CAREA are presented. The preliminary results showed how, during treatments, pesticide concentrations distant from the fields are significantly higher than background values.

Atmospheric Dispersion Modelling and Spatial Analysis to Evaluate Population Exposure to Pesticides from Farming Processes / Costanzini, Sofia; Teggi, Sergio; Bigi, Alessandro; Ghermandi, Grazia; Filippini, Tommaso; Malagoli, Carlotta; Nannini, Roberta; Vinceti, Marco. - In: ATMOSPHERE. - ISSN 2073-4433. - 9:2(2018), pp. 1-15. [10.3390/atmos9020038]

Atmospheric Dispersion Modelling and Spatial Analysis to Evaluate Population Exposure to Pesticides from Farming Processes

Costanzini, Sofia;Teggi, Sergio;Bigi, Alessandro;Ghermandi, Grazia;Filippini, Tommaso;Malagoli, Carlotta;Vinceti, Marco
2018

Abstract

This work originates from an epidemiological study aimed to assess the correlation between population exposure to pesticides used in agriculture and adverse health effects. In support of the population exposure evaluation two models implemented by the authors were applied: a GIS-based proximity model and the CAREA atmospheric dispersion model. In this work, the results of the two models are presented and compared. Despite the proximity analysis is widely used for these kinds of studies, it was investigated how meteorology could affect the exposure assessment. Both models were applied to pesticides emitted by 1519 agricultural fields and considering 2584 receptors distributed over an area of 8430 km2. CAREA output shows a considerable enhancement in the percentage of exposed receptors, from the 4% of the proximity model to the 54% of the CAREA model. Moreover, the spatial analysis of the results on a specific test site showed that the effects of meteorology considered by CAREA led to an anisotropic exposure distribution that differs considerably from the symmetric distribution resulting by the proximity model. In addition, the results of a field campaign for the definition and planning of ground measurement of concentration for the validation of CAREA are presented. The preliminary results showed how, during treatments, pesticide concentrations distant from the fields are significantly higher than background values.
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Atmospheric Dispersion Modelling and Spatial Analysis to Evaluate Population Exposure to Pesticides from Farming Processes / Costanzini, Sofia; Teggi, Sergio; Bigi, Alessandro; Ghermandi, Grazia; Filippini, Tommaso; Malagoli, Carlotta; Nannini, Roberta; Vinceti, Marco. - In: ATMOSPHERE. - ISSN 2073-4433. - 9:2(2018), pp. 1-15. [10.3390/atmos9020038]
Costanzini, Sofia; Teggi, Sergio; Bigi, Alessandro; Ghermandi, Grazia; Filippini, Tommaso; Malagoli, Carlotta; Nannini, Roberta; Vinceti, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1151494
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