Different "nongrain" activities affect the accumulation of heavy metals and their source-oriented health risks on cultivated lands.

"Nongrain" production on cultivated land is one of the primary environmental issues in China. Different "nongrain" activities may introduce different pollution sources to the local environment, leading to variations in heavy metal contents in soil, which can profoundly impact national food security. In this study, three typical "nongrain" regions (Nanxun (NX), Xiaoshan (XS) and Lin'an (LA)) with intensive aquaculture, tea planting and flower (seedling) growth on cultivated land around the Hangzhou metropolitan area were selected to address the spatial heterogeneity of accumulation levels, sources and source-oriented health risks of heavy metals in soil. The results showed that Hg was the main pollutant in NX and XS, while Cd and As were the major contaminants in LA. Aquiculture and sericultural industries (37.43%), natural sources (23.59%) and industrial activities (38.99%) were the major sources in NX; atmospheric deposition (37.73%), flower and seedling planting (23.49%) and metal-related industries (35.16%) were the major sources in XS; and atmospheric deposition (28.06%), excessive application of fertilizers and pesticides during tea planting (43.47%) and natural sources (28.47%) were the major sources in LA. The major risk population, area, exposure route and hazardous elements were children, LA, ingestion and As and Cr, respectively. From the perspective of source-based health risk assessment, in addition to natural sources that are difficult to intervene in, industrial activities, especially leather and wood process industries, metal-related industries and excessive fertilizer and pesticide application during tea planting contributed the most to the total health risk, which explained 67%, 41% and 42%, respectively, of the total risk in NX, XS and LA. High health risks are present in sources with heavy loadings of hazardous heavy metals (As and Cr); thus, to protect human health, the corresponding high-risk anthropogenic pollution sources in different "nongrain" areas need to be controlled.

Quantitative heterogeneous source apportionment of toxic metals through a hybrid method in spatial random fields.

Toxic metals in soils pose hazards to food security and human health. Accurate source apportionment provides foundation for pollution prevention. In this study, a novel hybrid method that combines positive matrix factorization, Bayesian maximum entropy and integrative predictability criterion is proposed to provide a new perspective for exploring the heterogeneity of pollution sources in spatial random fields. The results suggest that Cd, As and Cu are the predominant pollutants, with exceedance rates of 27%, 12% and 11%, respectively. The new method demonstrates superiority in predicting toxic metals when combined major and all sources as auxiliary information., with the improvements of 44% and 46%, respectively, Although the major sources identified with the hybrid method are the primary contributors to the accumulation of toxic metals (e.g. coal combustion for Hg, traffic emission for Pb and Zn, industrial activities for As, agricultural activities for Cd and Cu and natural sources for Cr and Ni), the impact of nonmajor sources on toxic metal sin specific regions should not be ignored (e.g. industrial activities on Ni, Pb and Zn in the north and natural sources on Cd, Cu, As, Pb and Zn in the south). For better pollution control, specific local sources should be considered.

Processing toxic metal source proxies appropriately for better spatial heterogeneity source apportionment.

Soil toxic metals have strong spatial heterogeneity, and their sources vary among regions. Thus, this study integrated the Catreg and geographically weighted regression (GWR) models to quantitatively extract the main source proxies (numerical and categorical variables were analyzed simultaneously) for different toxic metals and analyze the spatial heterogeneity of the distributions of these sources. Pb, Cd and Hg were the predominant toxic metals in soil. Of the samples with Pb, Cd and Hg, 84.12 %, 68.03 % and 41.57 % exceeded the background values, and 5.36 %, 6.42 % and 5.43 % were moderately contaminated according to the geoaccmulation index, respectively. Industrial activities, with relative importance values of 17.82 %, 31.54 % and 26.51 % for Cd, Hg and Pb, respectively, were the predominant source of these metals especially, in their high-content cluster areas (central urban areas). Soil available phosphorus was another important factor (relative importance values of 13.03 %, 13.41 % and 25.55 % for Cd, Hg and Pb, respectively), and agricultural activities (especially the overuse of phosphoric fertilizers) were identified as an anthropogenic source of these toxic metals. Soil parent material had the greatest influence on As and Cr, with relative importance values of 19.88 % and 19.09 %, respectively, especially in their high-content accumulation area (the eastern coastal area), indicating that these toxic metals mainly come from natural sources. Slope had important impacts on toxic metal accumulation (relative importance values of 17.48 %, 21.22 %, 12.40 % and 16.13 % for Cd, Hg, Cr and As, respectively) by influencing industrial distribution and pollutant migration. By changing the soil adsorption capacity, soil organic matter (explaining 13.01 % of Pb) and soil pH (explaining 14.58 % of As and 12.40 % of Cr) strongly influenced toxic metal accumulation. This study highlights the benefits of the integrated Catreg-GWR model for analyzing multiple spatially heterogeneous environmental data types (numerical and categorical variables), providing a potential foundation for local pollution prevention.

Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory.

Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.39, 84.29, 47.84, 49.75, 28.95, 18.01, 0.49 and 0.16 mg/kg, respectively. The thresholds were consistently greater than the Zhejiang Province backgrounds but lower than the national risk screening values, except for Cd. Approximately 27.9% of the samples were classified as contaminated at various levels, and they were distributed in the northern, northwestern and eastern regions of the study area. Additionally, 3.73%, 5.34% and 8.22% of the total area were classified as at-risk areas under confidence levels of 95%, 90% and 75%, respectively, through BME theory. The findings provide a reasonable classification system and suggestions for heavy metal pollution management and control.

Contamination assessment and source apportionment of heavy metals in agricultural soil through the synthesis of PMF and GeogDetector models.

Heavy metal pollution in soils has attracted great attention worldwide in recent decades. Selecting Hangzhou as a case study location, this research proposed the synthesis application of positive matrix factorization (PMF) and GeogDetector models for quantitative analysis of pollution sources, which is the basis for subsequent soil pollution prevention and remediation. In total, 2150 surface soil samples were collected across the study area. Although the mean concentrations of As, Cd, Cr, Hg, and Pb in the soils were lower than the National Environmental Quality Standards for Soils in China, the mean contents of As and Cd were higher than their corresponding local background values by approximately 1.31 and 1.59 times, respectively, indicating that heavy metals have been enriched in topsoil. Agricultural activities, industrial activities, and soil parent materials were the main sources of heavy metal pollution in the soils, accounting for 63.4%, 19.8%, and 16.8% of the total heavy metal accumulation, respectively. Cr was derived mainly from soil parent materials (80.72%). Cd was closely associated with agricultural activities (73.68%), such as sewage irrigation and application of fertilizer. Mercury was mainly attributed to industrial activities (92.38%), such as coal mining and smelting. As was related to agricultural (57.83%) and natural (35.56%) sources, and Pb was associated with industrial (42.42%) and natural (41.83%) sources. The new synthesis models are useful for estimating the source apportionment of heavy metals in soils.

The association between heavy metal soil pollution and stomach cancer: a case study in Hangzhou City, China.

Stomach cancer (SC) is a severe health burden, with nearly half of the world's cases found in China. Noticeably, the emissions of heavy metals into the environment have increased alongside rapid urbanization and industrialization in China. However, as regards carcinogenic associations, the relationship between heavy metals and SC is yet unclear. Based on 9378 newly diagnosed SC cases in Hangzhou City from 2009 to 2012, this work is concerned with the quantitative characterization of the spatial distribution pattern of SC incidence and its geographical association with soil heavy metals by means of a novel geographical model. The results show that (a) Cd is one of the severe soil pollutants in Hangzhou; (b) higher SC incidence clusters are in central Hangzhou, whereas lower clusters are found in the northeast and southwest with a male to female incidence ratio about 2.2:1; (c) although when considered separately, the heavy metals in this work do not have a considerable impact on the distribution of SC incidence in Hangzhou City, nevertheless, the joint effects of multiple heavy metals have significant impacts on SC risk. The present work calls for a rigorous quantitative assessment of the integrated heavy metal soil pollution and its effects on SC incidence.

Contribution of industrial density and socioeconomic status to the spatial distribution of thyroid cancer risk in Hangzhou, China.

BACKGROUND: The thyroid cancer (TC) incidence in China has increased dramatically during the last three decades. Typical in this respect is the case of Hangzhou city (China), where 7147 new TC cases were diagnosed during the period 2008-2012. Hence, the assessment of the TC incidence risk increase due to environmental exposure is an important public health matter. METHODS: Correlation analysis, Analysis of Variance (ANOVA) and Poisson regression were first used to evaluate the statistical association between TC and key risk factors (industrial density and socioeconomic status). Then, the Bayesian maximum entropy (BME) theory and the integrative disease predictability (IDP) criterion were combined to quantitatively assess both the overall and the spatially distributed strength of the "exposure-disease" association. RESULTS: Overall, higher socioeconomic status was positively correlated with higher TC risk (Pearson correlation coefficient=0.687, P<0.01). Compared to people of low socioeconomic status, people of median and high socioeconomic status showed higher TC risk: the Relative Risk (RR) and associated 95% confidence interval (CI) were found to be, respectively, RR=2.29 with 95% CI=1.99 to 2.63, and RR=3.67 with 95% CI=3.22 to 4.19. The "industrial density-TC incidence" correlation, however, was non-significant. Spatially, the "socioeconomic status-TC" association measured by the corresponding IDP coefficient was significant throughout the study area: the mean IDP value was -0.12 and the spatial IDP values were consistently negative at the township level. It was found that stronger associations were distributed among residents mainly on a stripe of land from northeast to southwest (consisting mainly of sub-district areas). The "industrial density-TC" association measured by its IDP coefficient was spatially non-consistent. CONCLUSIONS: Socioeconomic status is an important indicator of TC risk factor in Hangzhou (China) whose effect varies across space. Hence, socioeconomic status shows the highest TC risk effect in sub-district areas.

Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China.

Breast cancer (BC) is the main cause of death of female cancer patients in China. Mainstream mapping techniques, like spatiotemporal ordinary kriging (STOK), generate disease incidence maps that improve our understanding of disease distribution. Yet, the implementation of these techniques experiences substantive and technical complications (due mainly to the different characteristics of space and time). A new spatiotemporal projection (STP) technique that is free of the above complications was implemented to model the space-time distribution of BC incidence in Hangzhou city and to estimate incidence values at locations-times for which no BC data exist. For comparison, both the STP and the STOK techniques were used to generate BC incidence maps in Hangzhou. STP performed considerably better than STOK in terms of generating more accurate incidence maps showing a closer similarity to the observed incidence distribution, and providing an improved assessment of the space-time BC correlation structure. In sum, the inter-connections between space, time, BC incidence and spread velocity established by STP allow a more realistic representation of the actual incidence distribution, and generate incidence maps that are more accurate and more informative, at a lower computational cost and involving fewer approximations than the incidence maps produced by mainstream space-time techniques.

[Space-Time Estimations and Mapping of PM2.5 Fine Particulates Based on Multi-source Data].

PM2.5 pollution in China has become an extreme environmental and social problem and has generated widespread public concern. We estimate ground-level PM2.5 from satellite-derived aerosol optical depth (AOD), topography data, meteorological data, and pollutant emissions using a new technique, Bayesian maximum entropy (BME) combined with geographically weighted regression (GWR), to evaluate the spatial and temporal characteristics of PM2.5 exposure in an eastern region of China in winter. The overall 10-fold cross-validation R2 is 0.92, and the root mean squared prediction error (RMSE) is 8.32 µg·m-3. The mean prediction error (MPE) of the predicted monthly PM2.5 is -0.042 µg·m-3, the mean absolute prediction error (MAE) is 4.60 µg·m-3. Compared with the results of the Geographically Weighted Regression model-GWR (R2=0.71, RMSE=15.68 µg·m-3, MPE=-0.095 µg·m-3, MAE=11.14 µg·m-3), the prediction by the BME were greatly improved. In this location, the high PM2.5concentration area is mainly concentrated in North China, the Yangtze River Delta, and Sichuan Basin. The low concentration area is mainly concentrated in the south of China, including the Pearl River Delta and southwest of Yunnan. Temporally, there is migration trend from the coastal areas inland, and PM2.5 pollution is most serious in December 2015 and January 2016. It is relatively low in November 2015 and February 2016.

[Geographic spatial pattern of digestive system cancers in Yiwu city].

OBJECTIVE: To analyze the geographic spatial patterns and risk areas of main digestive system cancers in Yiwu city. METHODS: Newly diagnosed cases of esophageal, gastric and colorectal cancer during 2010-2014 were obtained from Yiwu Center for Disease Control and Prevention (CDC). The household registration population data in 2013 were obtained from public security bureau. Hierarchy clustering and partitioning regionalization method was used to generate geographic units. Global Moran's I was used to evaluate whether cancer incidence was significantly clustered in space, Anselin Local Moran's I was used to identify statistically significant hot spots, cold spots, and spatial outliers, and Spatial Scan Statistics was implemented to analyze the relative risk of cancers in different areas. RESULTS: The 5-year average incidence of esophageal, gastric and colorectal cancers were 9.99/100 000, 34.01/100 000 and 31.46/100 000, respectively. Males showed significantly higher incidence than females. The incidence was heterogeneous throughout the study area. Spatial Scan analysis revealed that southern Yiwu presented a significantly higher male esophageal cancer (RR=1.78) and gastric cancer (RR=1.87) risk. The central area of Yiwu showed a significantly lower female esophageal cancer risk (RR=0.00) and male stomach cancer risk (RR=0.63) and the northern Yiwu exhibited a significantly lower female colorectal cancer risk (RR=0.48). CONCLUSIONS: The incidence of main digestive tract cancers shows a heterogeneous distribution in Yiwu city.

Spatiotemporal Co-existence of Female Thyroid and Breast Cancers in Hangzhou, China.

Thyroid and breast cancers (TC, BC) are common female malignant tumors worldwide. Studies suggest that TC patients have a higher BC risk, and vice versa. However, it has not been investigated quantitatively if there is an association between the space-time TC and BC incidence distributions at the population level. This work aims to answer this question. 5358 TC and 8784 BC (female) cases were diagnosed in Hangzhou (China, 2008-2012). Pearson and Spearman rank correlation coefficients of the TC and BC incidences were high, and their patterns were geographically similar. The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions. Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data. IDP consistently demonstrated the spatiotemporal co-existence of TC and BC distributions throughout Hangzhou (2008-2012), which means that when the population experiences high incidences of one kind of cancer attention should be paid to the other kind of cancer too. The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy.

A Geographic Analysis about the Spatiotemporal Pattern of Breast Cancer in Hangzhou from 2008 to 2012.

BACKGROUND: Breast cancer (BC) is the most common female malignant tumor. Previous studies have suggested a big incidence disparity among different cities in China. The present work selected a typical city, Hangzhou, to study BC incidence disparity within the city. METHODS: Totally, 8784 female breast cancer cases were obtained from the Hangzhou Center for Disease Control and Prevention during the period 2008-2012. Analysis of Variance and Poisson Regression were the statistical tools implemented to compare incidence disparity in the space-time domain (reference group: township residents during 2008, area: subdistrict, town, and township, time frame: 2008-2012), space-time scan statistics was employed to detect significant spatiotemporal clusters of BC compared to the null hypothesis that the probability of cases diagnosed at a particular location was equal to the probability of cases diagnosed in the whole study area. Geographical Information System (GIS) was used to generate BC spatial distribution and cluster maps at the township level. RESULTS: The subdistrict populations were found to have the highest and most stable BC incidence. Although town and township populations had a relatively low incidence, it displayed a significant increasing trend from 2008 to 2012. The BC incidence distribution was spatially heterogeneous and clustered with a trend-surface from the southwest low area to the northeast high area. High clusters were located in the northeastern Hangzhou area, whereas low clusters were observed in the southwestern area during the time considered. CONCLUSIONS: Better healthcare service and lifestyle changes may be responsible for the increasing BC incidence observed in towns and townships. One high incidence cluster (Linping subdistrict) and two low incidence clusters (middle Hangzhou) were detected. The low clusters may be attributable mainly to developmental level disparity, whereas the high cluster could be associated with other risk factors, such as environmental pollution.