Sofia Costanzini

Passive exposure to agricultural pesticides and risk of childhood leukemia in an Italian community

BACKGROUND Exposure to pesticides has been suggested as a risk factor for childhood leukemia, but definitive evidence on this relation and the specific pesticides involved is still not clear. OBJECTIVE We carried out a population-based case-control study in a Northern Italy community to assess the possible relation between passive exposure to agricultural pesticides and risk of acute childhood leukemia. METHODS We assessed passive pesticide exposure of 111 childhood leukemia cases and 444 matched controls by determining density and type of agricultural land use within a 100-m radius buffer around childrens homes. We focused on four common crop types, arable, orchard, vineyard and vegetable, characterized by the use of specific pesticides that are potentially involved in childhood induced leukemia. The use of these pesticides was validated within the present study. We computed the odds ratios (OR) of the disease and their 95% confidence intervals (CI) according to type and density of crops around the childrens homes, also taking into account traffic pollution and high-voltage power line magnetic field exposure. RESULTS Childhood leukemia risk did not increase in relation with any of the crop types with the exception of arable crops, characterized by the use of 2.4-D, MCPA, glyphosate, dicamba, triazine and cypermethrin. The very few children (n=11) residing close to arable crops had an OR for childhood leukemia of 2.04 (95% CI 0.50-8.35), and such excess risk was further enhanced among children aged <5 years. CONCLUSIONS Despite the null association with most crop types and the statistical imprecision of the estimates, the increased leukemia risk among children residing close to arable crops indicates the need to further investigate the involvement in disease etiology of passive exposure to herbicides and pyrethroids, though such exposure is unlikely to play a role in the vast majority of cases.

A GIS-based atmospheric dispersion model for pollutants emitted by complex source areas

Gaussian dispersion models are widely used to simulate the concentrations and deposition fluxes of pollutants emitted by source areas. Very often, the calculation time limits the number of sources and receptors and the geometry of the sources must be simple and without holes. This paper presents CAREA, a new GIS-based Gaussian model for complex source areas. CAREA was coded in the Python language, and is largely based on a simplified formulation of the very popular and recognized AERMOD model. The model allows users to define in a GIS environment thousands of gridded or scattered receptors and thousands of complex sources with hundreds of vertices and holes. CAREA computes ground level, or near ground level, concentrations and dry deposition fluxes of pollutants. The input/output and the runs of the model can be completely managed in GIS environment (e.g. inside a GIS project). The paper presents the CAREA formulation and its applications to very complex test cases. The tests shows that the processing time are satisfactory and that the definition of sources and receptors and the output retrieval are quite easy in a GIS environment. CAREA and AERMOD are compared using simple and reproducible test cases. The comparison shows that CAREA satisfactorily reproduces AERMOD simulations and is considerably faster than AERMOD.

Remote sensing and GIS for the modeling of persistent organic pollutant in the marine environment

The characterization of the marine environment plays an important role in the understanding of the dynamics affecting the transport, fate and persistence (TFP) of Persistent Organic Pollutants (POPs). This work is part of a project funded by the Ministero dell’Istruzione, dell’Università e della Ricerca. The aim of the project is the assessment of the TFP of POPs in the Mediterranean sea. The analysis will be carried out at regionalmesoscale (central Mediterranean), and at local spatial scale considering different Italian test sites (the Delta of the Po River, the Venice Lagoon and the estuary of the Rio Nocella). The first step of this work involves the implementation of GIS geodatabases for the definition of the input dataset. The geodatabases were populated with MERIS and MODIS level 2 and level 3 products of Chlorophyll-a (CHL-a), Chromophoric Dissolved Organic Matter (CDOM), Aerosol Optical Thickness (AOT), Diffuse Attenuation Coefficient (DAC), Particulate Inorganic Carbon (PIC), Particulate Organic Carbon (POC) and Sea Surface Temperature (SST). The spatial scale (central Mediterranean sea) and the reference system (Plate Carrée projection) have been imposed as a constraint for the geodatabases. Four geodatabases have been implemented, two for MODIS and two for MERIS products with a monthly, seasonal and climatological temporal scale (2002 -2013). Here, we present a first application of a methodology aimed to identify vulnerable areas to POPs accumulation and persistence. The methodology allowed to assess the spatial distribution of the CHL-a in the central Mediterranean sea. The chlorophyll concentration is related to the amount of nutrients in the water and therefore provides an indicator of the potential presence of POPs. A pilot area of 300 x 200 km located in the North Adriatic sea has been initially considered. The seasonal and climatological MODIS and MERIS CHL-a variability were retrieved and compared with in-situ forcing parameters, i.e. Po River discharge rates and wind data. Study outlooks include a better accuracy of the distribution of the vulnerable areas achieved through the use of additional parameters (CDOM, SST, POC), and an assessment of the contribution of the contaminants by atmospheric dry deposition to the marine environment.

9 Risk of amyotrophic lateral sclerosis and passive residential exposure to pesticides: comparison of questionnaire-based with gis-based exposure assessment methods

Background/aim Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with still unknown aetiology. Among environmental factors, pesticides have been investigated due to their potential neurotoxic effects. Within a population-based case-control study conducted in two Italian regions, we aimed to investigate ALS risk due to passive residential exposure to pesticides using two methodologies. Methods The exposure assessment was carried with an individual questionnaire, which collected information of the entire residential history of subjects, focusing on rural residence or in the vicinity of agricultural areas. It was compared with assessment based on geographical information system (GIS), avoiding direct contact with study subjects. To do that, we computed the percentage (≥50%) of rural land use within the 100 m round buffer around each subjects’ residence, according to cover maps of two periods available from the Department of Agriculture, recent (2003–2009) and historical (1978–1989) ones. Risk for passive residential exposure to pesticides was computed using a sex and age adjusted logistic regression model for both methods, and their agreement was assessed using Cohen’s kappa (k). Results The odds ratio (OR) with their 95% confidence intervals (CI) for passive residential exposure to pesticides was 1.67 (95% CI 0.87 to 3.20) from the questionnaire-based assessment, while ORs from the GIS-based assessment were 1.05 (0.40 to 2.73) and 1.13 (0.49 to 2.63) for the recent and historical period, respectively. The agreement between two methods considering all participants was generally moderate to high, with k of 0.564 (95% CI: 0.361 to 0.767) and 0.648 (0.494–0.802) for recent and historical periods, respectively. Analyses divided between cases and controls yielded similar results, with k of 0.468 (0.133–0.803) in cases and 0.630 (0.382–0.879) in controls for recent period, and 0.642 (0.380–0.904) in cases and 0.652 (0.464–0.840) in controls for historical one. Conclusion Our results showed a slight increased risk of passive exposure to pesticides using the questionnaire-based assessment, with less conclusive results from the GIS-based one. The similar agreement either between periods and case/control status, suggested also that no substantial information bias and differential exposure misclassification occurred when assessing pesticides exposure in our population.

SPOT5 imagery for soil salinity assessment in Iraq

Soil salinization is a form of topsoil degradation due to the formation of soluble salts at deleterious levels. This phenomenon can seriously compromise vegetation health and agricultural productivity, and represents a worldwide environmental problem. Remote sensing is a very useful tool for soil salinization monitoring and assessment. In this work we show some results of a study aimed to define a methodology for soil salinity assessment in Iraq based on SPOT 5 imagery. This methodology allows the identification of salinized soils primarily on bare soils. Subsequently some soil salinity assessment can be done on vegetated soils. On bare soil the identification of salt is based on spectral analysis, using the Minimum Noise Fraction transformation and several indexes found in literature. In case of densely vegetated soils the methodology for the discrimination of salinized soils has been integrated with the results obtained from the classification of vegetation coverage.