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Environ Health ; 20(1): 101, 2021 09 06.
Artigo em Inglês | MEDLINE | ID: mdl-34488764


BACKGROUND: The outbreak of coronavirus disease (COVID-19) began in Wuhan, China in December 2019 and was declared a global pandemic on 11 March 2020. This study aimed to assess the effects of temperature and long-term exposure to air pollution on the COVID-19 mortality rate at the sub-national level in France. METHODS: This cross-sectional study considered different periods of the COVID-19 pandemic from May to December 2020. It included 96 departments (or NUTS 3) in mainland France. Data on long-term exposure to particulate matter (PM2.5), annual mean temperature, health services, health risk, and socio-spatial factors were used as covariates in negative binomial regression analysis to assess their influence on the COVID-19 mortality rate. All data were obtained from open-access sources. RESULTS: The cumulative COVID-19 mortality rate by department increased during the study period in metropolitan France-from 19.8/100,000 inhabitants (standard deviation (SD): 20.1) on 1 May 2020, to 65.4/100,000 inhabitants (SD: 39.4) on 31 December 2020. The rate was the highest in the departments where the annual average of long-term exposure to PM2.5 was high. The negative binomial regression models showed that a 1 µg/m3 increase in the annual average PM2.5 concentration was associated with a statistically significant increase in the COVID-19 mortality rate, corresponding to 24.4%, 25.8%, 26.4%, 26.7%, 27.1%, 25.8%, and 15.1% in May, June, July, August, September, October, and November, respectively. This association was no longer significant on 1 and 31 December 2020. The association between temperature and the COVID-19 mortality rate was only significant on 1 November, 1 December, and 31 December 2020. An increase of 1 °C in the average temperature was associated with a decrease in the COVID-19-mortality rate, corresponding to 9.7%, 13.3%, and 14.5% on 1 November, 1 December, and 31 December 2020, respectively. CONCLUSION: This study found significant associations between the COVID-19 mortality rate and long-term exposure to air pollution and temperature. However, these associations tended to decrease with the persistence of the pandemic and massive spread of the disease across the entire country.

Poluentes Atmosféricos/efeitos adversos , COVID-19/mortalidade , Exposição Ambiental/efeitos adversos , Material Particulado/efeitos adversos , Estudos Transversais , Exposição Ambiental/estatística & dados numéricos , França/epidemiologia , Humanos , Modelos Estatísticos , SARS-CoV-2 , Temperatura
Data Brief ; 35: 106939, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33686370


The COVID-19 pandemic has hit humanity, straining health care systems, economies, and governments worldwide. In one of the responses to the pandemic, a big global effort has been mounted to collect, analyze, and make data publicly available. However, many of the existing COVID-19 public datasets are (i) aggregated at country level, and (ii) tend not to bring the COVID-19-specific data coupled with socio-demographic, economic, public policy, health, pollution and environmental factors, all of which may be key elements to study the transmission of the SARS-CoV-2 and its severity. To aid the evaluation of the determinants and impact of the COVID-19 pandemic at a large scale, we present here a new dataset with socio-demographic, economic, public policy, health, pollution and environmental factors for the European Union at the small regions level (NUTS3). The database is freely accessible at This dataset can help to monitor the COVID-19 mortality and infections at the sub-national level and enable analysis that may inform future policymaking.

Data Brief ; 28: 105089, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31956677


Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel 5P remote sensing platform. As an example application, we applied the full workflow to process measurements of nitrogen dioxide (NO2) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4 × 7 km spatial resolution. The supplementary Python code package used to collect and process the data is made publicly available. The dataset provided in this article is of value for policy-makers and health assessment.

PLoS One ; 14(3): e0211964, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30840656


Urban change (urbanization) has dominated land change science for several decades. However, few studies have focused on what many scholars call the urban densification process (i.e., urban intensity expansion) despite its importance to both planning and subsequent impacts to the environment and local economies. This paper documents past urban densification patterns and uses this information to predict future densification trends in southeastern Wisconsin (SEWI) by using a rich dataset from the United States and by adapting the well-known Land Transformation Model (LTM) for this purpose. Urban densification is a significant and progressive process that often accompanies urbanization more generally. The increasing proportion of lower density areas, rather than higher density areas, was the main characteristic of the urban densification in SEWI from 2001 to 2011. We believe that improving urban land use efficiency to maintain rational densification are effective means toward a sustainable urban landscape. Multiple goodness-of-fit metrics demonstrated that the reconfigured LTM performed relatively well to simulate urban densification patterns in 2006 and 2011, enabling us to forecast densification to 2016 and 2021. The predicted future urban densification patterns are likely to be characterized by higher densities continue to increase at the expense of lower densities. We argue that detailed categories of urban density and specific relevant predictor variables are indispensable for densification prediction. Our study provides researchers working in land change science with important insights into urban densification process modeling. The outcome of this model can help planners to identify the current trajectory of urban development, enabling them to take informed action to promote planning objectives, which could benefit sustainable urbanization definitely.

Planejamento de Cidades/estatística & dados numéricos , Conservação dos Recursos Naturais/estatística & dados numéricos , Monitoramento Ambiental/estatística & dados numéricos , Urbanização/tendências , Humanos , Wisconsin