Modelling Spatial Heterogeneity in the Effects of Natural and Socioeconomic Factors, and Their Interactions, on Atmospheric PM2.5 Concentrations in China from 2000–2015

In recent years, atmospheric PM2.5 pollution in China has become increasingly severe and exploring the relationships among its influencing factors is important in the prevention and control of air pollution. Although previous studies have identified complexity in variations in PM2.5 concentrations and recognized the interaction of multiple factors, little quantitative information is available on the evolution of the relationships among these factors, their spatial heterogeneity, and the multiscale interactions between them. In this study, geographical detector and multiscale geographically weighted regression models have been used to explore the multiscale interactions among natural and socioeconomic factors and PM2.5 concentration in China over the period 2000–2015. The results indicate that the relationship between natural factors and PM2.5 concentration is stronger than that for socioeconomic factors. The type of interaction between each factor is dominated by bivariate and nonlinear enhancement, exhibiting strong interactions between natural factors and anthropogenic factors. Although the effect of each factor on PM2.5 is complex, the relative influence of both human activities and social factors is shown to have gradually increased over time and population, agriculture, urbanization, and socioeconomic activities in general make important contributions to PM2.5. In addition, the scale of effects related to natural factors is smaller and more stable compared to the influence of human activities during the period 2000-2015. There are significant differences in the way natural factors and socioeconomic factors affect PM2.5, and there is strong non-stationarity of spatial relationships. Factors associated with topography, vegetation (NDVI), climate (temperature), natural sources, and agricultural activity are shown to be important determinants of PM2.5 across China and warrant significant attention in terms of managing atmospheric pollution. The study demonstrates that spatial differences in the direction, intensity, and scale of each factor should be accounted for to improve prevention and control measures and alleviate regional PM2.5 pollution.

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Crimean-Congo hemorrhagic fever in Europe: current situation calls for preparedness

Eurosurveillance ◽

10.2807/ese.15.10.19504-en ◽

2010 ◽

Vol 15 (10) ◽

Author(s):

H C Maltezou ◽

L Andonova ◽

R Andraghetti ◽

M Bouloy ◽

O Ergonul ◽

...

Keyword(s):

Prevention And Control ◽

Agricultural Practices ◽

Hemorrhagic Fever ◽

Control Measures ◽

Anthropogenic Factors ◽

Virus Dynamics ◽

Crimean Congo Hemorrhagic Fever ◽

The Russian Federation ◽

Prevention And Control Measures ◽

And Control

During the last decade Crimean-Congo hemorrhagic fever (CCHF) emerged and/or re-emerged in several Balkan countries, Turkey, southwestern regions of the Russian Federation, and the Ukraine, with considerable high fatality rates. Reasons for re-emergence of CCHF include climate and anthropogenic factors such as changes in land use, agricultural practices or hunting activities, movement of livestock that may influence host-tick-virus dynamics. In order to be able to design prevention and control measures targeted at the disease, mapping of endemic areas and risk assessment for CCHF in Europe should be completed. Furthermore, areas at risk for further CCHF expansion should be identified and human, vector and animal surveillance be strengthened.

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PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

Atmosphere ◽

10.3390/atmos10020055 ◽

2019 ◽

Vol 10 (2) ◽

pp. 55 ◽

Cited By ~ 6

Author(s):

Guoliang Yun ◽

Yuanrong He ◽

Yuantong Jiang ◽

Panfeng Dou ◽

Shaoqing Dai

Keyword(s):

Spatial Distribution ◽

Socioeconomic Factors ◽

Yangtze River ◽

Yangtze River Delta ◽

River Delta ◽

Total Carbon ◽

The Yangtze River ◽

Natural Factors ◽

The Yangtze River Delta ◽

Pm2.5 Concentration

High concentrations of PM2.5 are a primary cause of haze in the lower atmosphere. A better understanding of the spatial heterogeneity and driving factors of PM2.5 concentrations is important for effective regional prevention and control. In this study, we carried out remote sensing inversion of PM2.5 concentration data over a long time series and used spatial statistical analyses and a geographical detector model to reveal the spatial distribution and variation characteristics of PM2.5 and the main influencing factors in the Yangtze River Delta from 2005 to 2015. Our results show that (1) The average annual PM2.5 concentration in the Yangtze River Delta prior to 2007 displayed an increasing trend, followed by a decreasing trend after 2007 which eventually stabilized; and (2) climate regionalization and geomorphology were the dominant natural factors driving PM2.5 concentration diffusion, while total carbon dioxide emissions and population density were the dominant socioeconomic factors affecting the formation of PM2.5. Natural factors and socioeconomic factors together lead to PM2.5 pollution. These findings provide an interpretation of PM2.5 spatial distribution and the mechanisms influencing PM2.5 pollution, which can help the Chinese government develop effective abatement strategies.

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Zoonotic Diseases: Etiology, Impact, and Control

Microorganisms ◽

10.3390/microorganisms8091405 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1405 ◽

Cited By ~ 3

Author(s):

Md. Tanvir Rahman ◽

Md. Abdus Sobur ◽

Md. Saiful Islam ◽

Samina Ievy ◽

Md. Jannat Hossain ◽

...

Keyword(s):

Zoonotic Disease ◽

Zoonotic Diseases ◽

Control Measures ◽

Anthropogenic Factors ◽

Human Pathogens ◽

Effective Prevention ◽

Travel And Tourism ◽

Health Measures ◽

Natural Factors ◽

And Control

Most humans are in contact with animals in a way or another. A zoonotic disease is a disease or infection that can be transmitted naturally from vertebrate animals to humans or from humans to vertebrate animals. More than 60% of human pathogens are zoonotic in origin. This includes a wide variety of bacteria, viruses, fungi, protozoa, parasites, and other pathogens. Factors such as climate change, urbanization, animal migration and trade, travel and tourism, vector biology, anthropogenic factors, and natural factors have greatly influenced the emergence, re-emergence, distribution, and patterns of zoonoses. As time goes on, there are more emerging and re-emerging zoonotic diseases. In this review, we reviewed the etiology of major zoonotic diseases, their impact on human health, and control measures for better management. We also highlighted COVID-19, a newly emerging zoonotic disease of likely bat origin that has affected millions of humans along with devastating global consequences. The implementation of One Health measures is highly recommended for the effective prevention and control of possible zoonosis.

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Assessing Spatial Heterogeneity of Factor Interactions on PM2.5 Concentrations in Chinese Cities

Remote Sensing ◽

10.3390/rs13245079 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5079

Author(s):

Yuhao Jin ◽

Han Zhang ◽

Hong Shi ◽

Huilin Wang ◽

Zhenfeng Wei ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Socioeconomic Factors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fine Particulate Matter ◽

Pollution Prevention ◽

Driving Factors ◽

Geographically Weighted Regression Model ◽

Governance Strategies ◽

And Control

The identification of fine particulate matter (PM2.5) concentrations and its driving factors are crucial for air pollution prevention and control. The factors that influence PM2.5 in different regions exhibit significant spatial heterogeneity. Current research has quantified the spatial heterogeneity of single factors but fails to discuss the interactions between factors. In this study, we first divided the study area into subregions based on the spatial heterogeneity of factors in a multi-scale geographically weighted regression model. We then investigated the interactions between different factors in the subregions using the geographical detector model. The results indicate that there was significant spatial heterogeneity in the interactions between the driving factors of PM2.5. The interactions between natural factors have significant uncertainty, as do those between the normalized difference vegetation index (NDVI) and socioeconomic factors. The interactions between socioeconomic factors in the subregions were consistent with those in the whole region. Our findings are expected to deepen the understanding of the mechanisms at play among the aforementioned drivers and aid policymakers in adopting unique governance strategies across different regions.

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