Temporal and spatial variations in the sub-daily precipitation structure over the Qinghai-Tibet Plateau (QTP).

Precipitation is a vital component of the global atmospheric and hydrological cycles and influencing the distribution of water resources. Even subtle changes in precipitation can significantly impact ecosystems, energy cycles, agricultural production, and food security. Therefore, understanding the changes in the precipitation structure under climate change is essential. The Qinghai-Tibet Plateau (QTP) is a region sensitive to global climate change and profoundly impacts the atmospheric water cycle in Asia and even globally, rendering it a hot topic in climate change research in recent years. Few studies have examined on the sub-daily scale precipitation structure over the QTP. In this paper, the characteristics of sub-daily precipitation on the QTP were systematically investigated from multiple perspectives, including the concentration index, skewness (the third standardized moment of a distribution), and kurtosis (the fourth standardized moment of a distribution). The results indicated that the frequency of moderate-intensity nighttime precipitation on the QTP generally increased, and the analysis of both the concentration index and kurtosis (skewness) suggested that extreme precipitation was more frequent in the southwestern foothills of the QTP. Furthermore, potential high-risk areas for natural disasters were identified on the QTP, and found that the southeastern part of the plateau constituted a potential hotspot area for flood disasters. Given the complexity of climate change, a comprehensive analysis of the spatiotemporal characteristics of diurnal and nighttime precipitation changes on the QTP could help reveal the regularity of precipitation changes. This has significant implications for forecasting, warning, disaster preparedness, and mitigation efforts on the QTP.

A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure.

Radon is a radioactive gas and a major source of ionizing radiation exposure for humans. Consequently, it can pose serious health threats when it accumulates in confined environments. In Europe, recent legislation has been adopted to address radon exposure in dwellings; this law establishes national reference levels and guidelines for defining Radon Priority Areas (RPAs). This study focuses on mapping the Geogenic Radon Potential (GRP) as a foundation for identifying RPAs and, consequently, assessing radon risk in indoor environments. Here, GRP is proposed as a hazard indicator, indicating the potential for radon to enter buildings from geological sources. Various approaches, including multivariate geospatial analysis and the application of artificial intelligence algorithms, have been utilised to generate continuous spatial maps of GRP based on point measurements. In this study, we employed a robust multivariate machine learning algorithm (Random Forest) to create the GRP map of the central sector of the Pusteria Valley, incorporating other variables from census tracts such as land use as a vulnerability factor, and population as an exposure factor to create the risk map. The Pusteria Valley in northern Italy was chosen as the pilot site due to its well-known geological, structural, and geochemical features. The results indicate that high Rn risk areas are associated with high GRP values, as well as residential areas and high population density. Starting with the GRP map (e.g., Rn hazard), a new geological-based definition of the RPAs is proposed as fundamental tool for mapping Collective Radon Risk Areas in line with the main objective of European regulations, which is to differentiate them from Individual Risk Areas.

Epidemiology, risk areas and macro determinants of gastric cancer: a study based on geospatial analysis.

BACKGROUND: Both incidence and mortality of gastric cancer in Gansu rank first in china, this study aimed to describe the recent prevalence of gastric cancer and explore the social and environmental determinants of gastric cancer in Gansu Province. METHODS: The incidence of gastric cancer in each city of Gansu Province was calculated by utilizing clinical data from patients with gastric cancer (2013-2021) sourced from the medical big data platform of the Gansu Province Health Commission, and demographic data provided by the Gansu Province Bureau of Statistics. Subsequently, we conducted joinpoint regression analysis, spatial auto-correlation analysis, space-time scanning analysis, as well as an exploration into the correlation between social and environmental factors and GC incidence in Gansu Province with Joinpoint_5.0, ArcGIS_10.8, GeoDa, SaTScanTM_10.1.1 and GeoDetector_2018. RESULTS: A total of 75,522 cases of gastric cancer were included in this study. Our findings suggested a significant upward trend in the incidence of gastric cancer over the past nine years. Notably, Wuwei, Zhangye and Jinchang had the highest incidence rates while Longnan, Qingyang and Jiayuguan had the lowest. In spatial analysis, we have identified significant high-high cluster areas and delineated two high-risk regions as well as one low-risk region for gastric cancer in Gansu. Furthermore, our findings suggested that several social and environmental determinants such as medical resource allocation, regional economic development and climate conditions exerted significant influence on the incidence of gastric cancer. CONCLUSIONS: Gastric cancer remains an enormous threat to people in Gansu Province, the significant risk areas, social and environmental determinants were observed in this study, which may improve our understanding of gastric cancer epidemiology and help guide public health interventions in Gansu Province.

SuperMap-Based Research on the Spread Situation and Visual Monitoring Platform of Major Health Events.

To present information on infectious diseases caused by viruses clearly and track the changes of data in real-time, data visualization can be used preferentially considering that it can identify problems behind data accurately. In this paper, based on the SuperMap Online platform and Tianditu, a national platform for common geospatial information services, a risk level map of infectious diseases distribution area is made by Web GIS and cartography. Meanwhile, the platform plays an important role in information collection, management, analysis, prevention and control, and release of measures when a major health event spreads. The method shows many advantages, such as various visualization means, ease to be published and shared, simple operation, and programming realization, which may be taken as technical references for solving the same type of visualization application problems. The research also facilitates the data visualization and monitoring of the spread of infectious diseases in major health events, and can effectively provide services for monitoring, decision-making, dispatching, and handling the spread of infectious diseases.

Predicting Disparity between ASF-Managed Areas and Wild Boar Habitats: A Case of South Korea.

African swine fever (ASF) is a highly contagious viral disease affecting both domestic and wild boars. Since its first outbreak in South Korea in 2019, substantial efforts have been made to prevent ASF transmission by reducing the wild boar population and eliminating infected carcasses; however, the persistence of ASF transmission has posed challenges to these efforts. To improve ASF management strategies, the limitations of current management strategies must be identified by considering disparities between wild boar habitats and ASF-managed areas with environmental and anthropogenic characteristics of wild boars and their management strategies. Here, ensemble species distribution models were used to estimate wild boar habitats and potential ASF-managed areas, with elevation, distance to urban areas, and Normalized Difference Vegetation Index as important variables. Binary maps of wild boar habitats and potential ASF-managed areas were generated using the maxSSS as the threshold criterion. Disparity areas of ASF management were identified by overlying regions evaluated as wild boar habitats with those not classified as ASF-managed areas. Dense forests near urban regions like Chungcheongbuk-do, Gyeongsangbuk-do, and Gyeongsangnam-do were evaluated as disparity areas having high risk of ASF transmission. These findings hold significant potential for refining ASF management strategies and establishing proactive control measures.

Epidemiological analysis and risk prediction of scrub typhus from 2006 to 2021 in Sichuan, China.

Background: In the past decade, the number of reported cases of scrub typhus (ST) has increased dramatically in Sichuan Province. We aimed to overview the epidemiological characteristics of ST, identify the variables contributing to the spatial distribution, and estimate the risk areas of ST occurrence. Methods: Daily ST cases reported at the county level from 2006 to 2021 and datasets on environmental and socioeconomic variables were obtained. Joinpoint regression model was utilized to examine the incidence trends and to calculate the annual percentage change. Global spatial autocorrelation analysis was employed to explore the spatial temporal patterns. Then BRT model was employed to identify variables that make sense and predict the risk areas of ST occurrence. Result: It has been reported that there were 6,338 ST cases in Sichuan Province from 2006 to 2021, and the incidence rates continued to rise. Most cases were distributed between June and October each year, peaking in August. During the study period, the cases showed spatial clustering at the county level, mainly in the Panxi area, and then slowly spread to the northwest and northeast. Shrubs, precipitation, farmland and maximum temperature were the primary variables that affected the spatial distribution of this disease. It was estimated that the areas including Liangshan, Panzhihua, Bazhong, and Guangyuan were most at risk of transmission. and there were approximately 32.315 million people living in the areas with potential risk of infection throughout Sichuan. Conclusion: Many counties in Sichuan Province were estimated to be susceptible to ST. Our found in this data-driven study could be used to guide the implementation of targeted prevention and control measures in high-risk areas.

Factors Associated with Vaccine Refusal (Polio and Routine Immunization) in High-Risk Areas of Pakistan: A Matched Case-Control Study.

BACKGROUND: Pakistan has subpar childhood immunization rates and immunization activities have faced several challenges over the past years. We evaluated the social-behavioral and cultural barriers and risk factors for refusal of polio, Routine Immunization (RI), or both in high-risk areas of poliovirus circulation. METHODS: A matched case-control study was conducted from April to July 2017 in eight super high-risk Union Councils of five towns in Karachi, Pakistan. A total of 3 groups, each with 250 cases, including refusals for the Oral Polio Vaccine (OPV) in campaigns (national immunization days and supplementary immunization activities), RI, and both, were matched with 500 controls and identified using surveillance records. Sociodemographic characteristics, household information, and immunization history were assessed. Study outcomes included social-behavioral and cultural barriers and reasons for vaccine refusal. Data were analyzed in STATA using conditional logistic regression. RESULTS: RI refusal was associated with illiteracy and fear of the vaccine's adverse effects, while OPV refusals were linked to the mother's decision authority and the assumption that the OPV caused infertility. Conversely, higher socioeconomic status (SES) and knowledge of and willingness to vaccinate with Inactivated Polio Vaccine (IPV) were inversely associated with RI; and lower SES, walking to the vaccination point, knowledge of IPV, and an understanding of contracting polio were inversely associated with OPV refusals, with the latter two also inversely associated with complete vaccine refusal. CONCLUSION: Education, knowledge and understanding of vaccines, and socioeconomic determinants influenced OPV and RI refusals among children. Effective interventions are needed to address knowledge gaps and misconceptions among parents.

Optimal starting age of endoscopic screening for esophageal cancer in China: A multicenter prospective cohort study.

BACKGROUND: Although endoscopic screening for esophageal cancer has been performed in high-risk areas in China for decades, there is limited and inconsistent evidence regarding the starting age for individuals participating in screening. The aim of this study is to investigate the optimal starting age of esophageal cancer screening. METHODS: This study is based on a multicenter prospective cohort consisting 338,017 permanent residents aged 40-69 years in six high-risk areas of esophageal cancer in China. The participation rate, detection rate, hazard ratios (HRs), cumulative incidence and mortality and number needed to screen (NNS) were calculated in each age group. Screening burden, benefit and risk were compared among screening strategies with different initiation ages to explore the optimal starting age for population-based screening in high-risk areas. RESULTS: Individuals aged 50-69 had a higher participation rate, a higher detection rate and improved screening effectiveness than those aged 40-49. The endoscopic screening had no significant effect on reducing the incidence of esophageal cancer in individuals under 55 and mortality in individuals under 45. Increasing the starting age to 50 years reduced the screening demand and NNS by 40% and 55%, and resulted in 12% of detectable positive cases, 16% of preventable incident cases, and 14% of preventable deaths being missed. CONCLUSIONS: Postponing the starting age of endoscopic screening to 50 years might yield a more-favorable balance between screening benefit and burden in high- risk areas with limited resources.

Spatio-temporal relative risks and priority areas for visceral leishmaniasis control in Brazil, between 2001 and 2020.

Visceral leishmaniasis (VL) is a pressing public health problem in Brazil. The proper implementation of disease control programs in priority areas is a challenge for healthcare managers. The present study aimed to analyze the spatio-temporal distribution and identify high risk areas of VL occurrence in the Brazilian territory. We analyzed data regarding new cases with confirmed diagnosis of VL in Brazilian municipalities, from 2001 to 2020, extracted from the Brazilian Information System for Notifiable Diseases. The Local Index of Spatial Autocorrelation (LISA) was used to identify contiguous areas with high incidence rates in different periods of the temporal series. Clusters of high spatio-temporal relative risks were identified using the scan statistics. The accumulated incidence rate in the analyzed period was 33.53 cases per 100,000 inhabitants. The number of municipalities that reported cases showed an upward trend from 2001 onward, although there was a decrease in 2019 and 2020. According to LISA, the number of municipalities considered a priority increased in Brazil and in most states. Priority municipalities were predominantly concentrated in the states of Tocantins, Maranhão, Piauí, and Mato Grosso do Sul, in addition to more specific areas of Pará, Ceará, Piauí, Alagoas, Pernambuco, Bahia, São Paulo, Minas Gerais, and Roraima. The spatio-temporal clusters of high-risk areas varied throughout the time series and were relatively higher in the North and Northeast regions. Recent high-risk areas were found in Roraima and municipalities in northeastern states. VL expanded territorially in Brazil in the 21st century. However, there is still a considerable spatial concentration of cases. The areas identified in the present study should be prioritized for disease control actions.

A mental health survey among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China.

Background: The sporadic coronavirus disease (COVID-19) epidemic has placed enormous psychological stress on people, especially clinicians. The objective of this study was to examine depression, anxiety, quality of life (QOL), and related social psychological factors among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China and to provide a reference for formulating reasonable countermeasures. Methods: In this cross-sectional study, demographic information, COVID-19-related questions, anxiety (Generalized Anxiety Disorder-7, GAD-7), depression (Patient Health Questionnaire-9, PHQ-9), insomnia (Insomnia Severity Index, ISI), stress (Perceived Stress Scale-10, PSS-10), and QOL (World Health Organization Quality of Life-brief version, WHOQOL-BREF) were collected. Binary logistic regression analysis was used to test the relationships between anxiety and/or depression and other related problems. Multiple linear regression analysis was used to test the relationships among factors influencing QOL. Results: A total of 146 young front-line clinicians were included. The prevalence rates of depression, anxiety, and anxiety-depression comorbidity were 37.7% (95% CI = 29.7-45.6%), 26.0% (95% CI = 18.8-33.2%), and 24.0% (95% CI = 17.0-31.0%), respectively. Severe stress (OR = 1.258, 95% CI = 1.098-1.442, P < 0.01) and insomnia (OR = 1.282, 95% CI = 1.135-1.447, P < 0.01) were positively correlated with depression. Severe stress (OR = 1.487, 95% CI = 1.213-1.823, P < 0.01) and insomnia (OR = 1.131, 95% CI = 1.003-1.274, P < 0.05) were positively correlated with anxiety. Severe stress (OR = 1.532, 95% CI = 1.228-1.912, P < 0.01) was positively correlated with anxiety-depression comorbidity. However, insomnia (OR = 1.081, 95% CI = 0.963-1.214, P > 0.05) was not correlated with anxiety-depression comorbidity. The belief that the vaccine will stop the COVID-19 pandemic (OR = 0.099, 95% CI = 0.014-0.715, P < 0.05) was negatively correlated with anxiety and anxiety-depression comorbidity (OR = 0.101, 95% CI = 0.014-0.744, P < 0.05). Severe stress (B = -0.068, 95% CI = -0.129 to -0.007, P < 0.05) and insomnia (B = -0.127, 95% CI = -0.188 to -0.067, P < 0.01) were negatively correlated with QOL. The belief that the vaccine could provide protection (B = 1.442, 95% CI = 0.253-2.631, P < 0.05) was positively correlated with QOL. Conclusions: The prevalence of depression, anxiety, and even anxiety-depression comorbidity was high among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China. Various biological and psychological factors as well as COVID-19-related factors were associated with mental health issues and QOL. Psychological intervention should evaluate these related factors and formulate measures for these high-risk groups.

Epidemic characteristics and transmission risk prediction of brucellosis in Xi'an city, Northwest China.

Human brucellosis (HB) has re-emerged in China since the mid-1990s, and exhibited an apparent geographic expansion shifted from the traditional livestock regions to the inland areas of China. It is often neglected in non-traditional epidemic areas, posing a serious threat to public health in big cities. We carried out a retrospective epidemiological study in Xi'an, the largest city in northwestern China. It utilizes long-term surveillance data on HB during 2008-2021 and investigation data during 2014-2021. A total of 1989 HB cases were reported in Xi'an, consisting of 505 local cases, i.e., those located in Xi'an and 1,484 non-local cases, i.e., those located in other cities. Significantly epidemiological heterogeneity was observed between them, mainly owing to differences in the gender, occupation, diagnostic delays, and reporting institutions. Serological investigations suggested that 59 people and 1,822 animals (sheep, cattle, and cows) tested positive for brucellosis from 2014 to 2021, with the annual average seroprevalence rates were 1.38 and 1.54%, respectively. The annual animal seroprevalence rate was positively correlated with the annual incidence of non-local HB cases. Multivariate boosted regression tree models revealed that gross domestic product, population density, length of township roads, number of farms, and nighttime lights substantially contributed to the spatial distribution of local HB. Approximately 7.84 million people inhabited the potential infection risk zones in Xi'an. Our study highlights the reemergence of HB in non-epidemic areas and provides a baseline for large and medium-sized cities to identify regions, where prevention and control efforts should be prioritized in the future.

Accumulation pattern and risk assessment of potentially toxic elements in selected wastewater-irrigated soils and plants in Vehari, Pakistan.

There are scarce data about the accumulation pattern and risk assessment of potentially toxic elements (PTEs) in soil and associated potential ecological risks, especially in less-developed countries. This study aims to assess the pollution levels and potential ecological risks of PTEs (As, Cr, Cd, Cu, Ni, Mn, Pb and Zn) in wastewater-irrigated arable soils and different edible-grown plants in selected areas of Vehari, Pakistan. The results revealed that the values of PTEs in soil samples were higher than their respective limit values by 20% for As, 87% for Cd, 15% for Cu, 2% for Cr, 83% for Mn, 98% for Fe, and 7% for Zn. The values of soil risk indices such as the potential ecological risk (PERI >380 for all samples), pollution load index (PLI >4 for 94% of studied samples), and degree of contamination (Dc > 24 for all samples) showed severe soil contamination in the study area. Some vegetables exhibited a high metal accumulation index (e.g., 8.1 for onion), signifying potential associated health hazards. Thus, long-term wastewater irrigation has led to severe soil contamination, which can pose potential ecological risks via PTE accumulation in crops, particularly Cd. Therefore, to ensure food safety, frequent wastewater irrigation practices need to be minimized and managed in the study area.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.

The Role of the Wild Boar Spreading African Swine Fever Virus in Asia: Another Underestimated Problem.

African swine fever (ASF) is a highly lethal infectious disease in naive populations of domestic pigs and wild boar. In Asia, from the first outbreak in August 2018 until the end of November 2021, ASF has been reported in 16 Asian countries. The ASF virus (ASFV) circulation in domestic pigs is considered the main problem in Asia. On the other hand, there are very few reports of ASF in wild boar in this region. However, considering the high wild boar density within the same area of smallholder domestic pig farms in Asia, the occurrence of ASFV infection in wild boar may be underestimated. The role of the wild boar in other ASF epidemiological scenarios, such as Europe, is a key for the maintenance and transmission of the disease. Hence, we performed a preliminary study estimating the extent of ASFV infection in the Asian wild boar population. The potential risk area of ASF-infected wild boar was calculated based on the habitat suitability for wild boar, the kernel density of ASF notification in smallholder farms and wild boar, and the ASFV transmission rate of wild boar. As a result of the analysis, high-, medium-, and low-risk areas were identified throughout Southeast and East Asia. The highest risk area was detected in China, followed by Myanmar, Far East Russia, Thailand, Vietnam, Laos, Cambodia, and the Philippines. Additionally, another risk area was detected from northeastern China to the Korean Peninsula, including Far East Russia. This study shows hot spots where a high risk of infection in wild boar is most likely to occur, helping to control ASF.

Vegetation indices' spatial prediction based novel algorithm for determining tsunami risk areas and risk values.

This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with k-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (i.e., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%.

Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review.

The COVID-19 pandemic has posed a significant global health threat since January 2020. Policies to reduce human mobility have been recognized to effectively control the spread of COVID-19; although the relationship between mobility, policy implementation, and virus spread remains contentious, with no clear pattern for how countries classify each other, and determine the destinations to- and from which to restrict travel. In this rapid review, we identified country classification schemes for high-risk COVID-19 areas and associated policies which mirrored the dynamic situation in 2020, with the aim of identifying any patterns that could indicate the effectiveness of such policies. We searched academic databases, including PubMed, Scopus, medRxiv, Google Scholar, and EMBASE. We also consulted web pages of the relevant government institutions in all countries. This rapid review's searches were conducted between October 2020 and December 2021. Web scraping of policy documents yielded additional 43 country reports on high-risk area classification schemes. In 43 countries from which relevant reports were identified, six issued domestic classification schemes. International classification schemes were issued by the remaining 38 countries, and these mainly used case incidence per 100,000 inhabitants as key indicator. The case incidence cut-off also varied across the countries, ranging from 20 cases per 100,000 inhabitants in the past 7 days to more than 100 cases per 100,000 inhabitants in the past 28 days. The criteria used for defining high-risk areas varied across countries, including case count, positivity rate, composite risk scores, community transmission and satisfactory laboratory testing. Countries either used case incidence in the past 7, 14 or 28 days. The resulting policies included restrictions on internal movement and international travel. The quarantine policies can be summarized into three categories: (1) 14 days self-isolation, (2) 10 days self-isolation and (3) 14 days compulsory isolation.

Geochemical characteristics and source apportionment of toxic elements in the Tethys-Himalaya tectonic domain, Tibet, China.

Toxic elements (TEs) in soil threaten the eco-environmental system and human health. The identification and prediction of sources and high-risk areas of TEs in soil are fundamental for regional pollution prevention and control. In this study, geostatistical methods and GIS-based approaches were used to quantitatively analyze the spatial distribution, geochemical characteristics, key driving factors, and their interactive effects of TEs in soil from a typical area of the Tethys-Himalaya tectonic domain in Tibet based on an integrated approach combining positive matrix factorization and GeoDetector models. The mean contents of chromium, arsenic (As), cadmium, mercury and lead in the soil exceeded the Tibetan background values, with 66.20% of As being higher than the screening values. The spatial distribution of TEs content in the soil was primarily affected by geogenic source factors (primarily geology types, soil parent materials, soil types, and soil pH), and environmental source factors (primarily precipitation and vegetation types) and anthropogenic source factors (primarily income of residents and land-use types) also had the same contribution approximately. Compared with that for individual driving factors, the interaction between most pairs of driving factors enhanced their explanatory power. The high-risk areas for soil As pollution were primarily distributed in the valley areas of the upper reaches of the Longzi River Basin. Therefore, to guarantee the health of residents and the security and sustainability of agricultural production in the study area, regular monitoring and soil remediation should be used to reduce the migration and transformation of As in the local biogeochemical cycle. This study provides new ideas for the regional prediction of high-risk areas for soil pollution, which has guiding importance and reference value for the control and management of large-scale soil pollution.

Predictive modeling of sand fly distribution incriminated in the transmission of Leishmania (Viannia) braziliensis and the incidence of Cutaneous Leishmaniasis in the state of Paraná, Brazil.

Southern Brazil concentrates a considerable number of cases of cutaneous leishmaniasis reported since 1980, and Paraná is the state that most records CL cases in the region. The main sand fly species incriminated as vectors of Leishmania (Viannia) braziliensis (Vianna,1911) are Migonemyia (Migonemyia) migonei (França, 1920), Nyssomyia (Nyssomyia) neivai (Pinto, 1926) and Nyssomyia (Nyssomyia) whitmani (Antunes & Coutinho, 1936). In this study, we evaluated areas with climatic suitability for the distribution of these vectors and correlated these data with CL incidence in the state. The occurrence points of Mg. migonei, Ny. neivai, and Ny. whitmani were extracted from a literature review and field data. For CL analysis in the state of Paraná, data were obtained from the Informatics Department of the Unified Health System of Brazil (DATASUS), covering the period from 2001 to 2019. The layers of bioclimatic variables from the WorldClim database were used in the study. Species distribution modeling was developed using the MaxEnt Software version 3.4.4. ArcGIS software version 10.5 was used to develop suitability maps and the graphical representation of disease incidence. The AUC values were acceptable for all models (> 0,8). Bioclimatic variables BIO13 and BIO14 were the most influential in the distribution of Mg. migonei, while BIO19 and BIO6 were the variables that most influenced the distribution of Ny. neivai, and Ny. whitmani was most influenced by variables BIO5 and BIO9. During 19 years, 4992 cases of CL were reported in the state by 286 municipalities (71,6%). Northern Paraná showed the highest number of areas with very high and high climatic suitability for the occurrence of these species, coinciding with the highest number of CL cases. The modeling tools allowed analyzing the association between climatic variables and the geographical distribution of CL in the state. Moreover, they provided a better understanding of the climatic conditions related to the distribution of different species, favoring the monitoring of risk areas, the implementation of preventive measures, risk awareness, early and accurate diagnosis, and consequent timely treatment.

Prediction of high-risk areas of soil heavy metal pollution with multiple factors on a large scale in industrial agglomeration areas.

Heavy metals in soil are a great threat to ecosystems and human health. The rapid development of industrialization has created a serious risk of heavy metal pollution in soil. The study took the industrial-intensive Dahetan subbasin as the typical area. The factors and interactions that affected the distribution of soil heavy metals (Cd, Hg, As, Pb and Cr) in the typical area were explored based on the Geodetector model. The analysis results were extended to predict high-risk areas of heavy metal pollution in the soil in the Xiangjiang River basin. The results showed that Cd, As and Pb were significantly affected by local industrial and mining activities, and Hg and Cr were primarily affected by natural factors, such as pH and soil type. Compared to a single factor, the interaction between factors had a greater impact on the concentration of heavy metals. The high-risk areas of soil heavy metal pollution in the Xiangjiang River basin were primarily concentrated in the upper reaches and middle reaches.Significant overlapping of high-risk areas of multiple heavy metals occurred in the west, middle and south of the basin. The spatial visualization of the high-risk areas was realized, and the influence of several factors was integrated via layer superposition. This study proposes a new idea to predict the high-risk areas of soil pollution in large-scale areas to provide a reference for the regional prevention and control of soil pollution.

Assessment of foot-and-mouth disease risk areas in mainland China based spatial multi-criteria decision analysis.

BACKGROUND: Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hoofed animals. As a transboundary animal disease, the prevention and control of FMD are important. This study was based on spatial multi-criteria decision analysis (MCDA) to assess FMD risk areas in mainland China. Ten risk factors were identified for constructing risk maps by scoring, and the analytic hierarchy process (AHP) was used to calculate the criteria weights of all factors. Different risk factors had different units and attributes, and fuzzy membership was used to standardize the risk factors. The weighted linear combination (WLC) and one-at-a-time (OAT) were used to obtain risk and uncertainty maps as well as to perform sensitivity analysis. RESULTS: Four major risk areas were identified in mainland China, including western (parts of Xinjiang and Tibet), southern (parts of Yunnan, Guizhou, Guangxi, Sichuan and Guangdong), northern (parts of Gansu, Ningxia and Inner Mongolia), and eastern (parts of Hebei, Henan, Anhui, Jiangsu and Shandong). Spring is the main season for FMD outbreaks. Risk areas were associated with the distance to previous outbreak points, grazing areas and cattle density. Receiver operating characteristic (ROC) analysis indicated that the risk map had good predictive power (AUC=0.8634). CONCLUSIONS: These results can be used to delineate FMD risk areas in mainland China, and veterinary services can adopt the targeted preventive measures and control strategies.