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With the rapid development of the mining industry, the pollution of heavy metal(loid)s in soils near copper (Cu) mining sites is a significant concern worldwide. However, the pollution status and probabilistic health risks of heavy metal(loid)s of soils associated with Cu mines, have rarely been studied on a global scale. In this study, eight heavy metal(loid) concentrations in soil samples taken near 102 Cu mining sites worldwide were obtained through a literature review. Based on this database, the heavy metal(loid) pollution and ecological risk in soils near Cu mines were evaluated. Most of the study sites exceeded the moderately to heavily polluted levels of Cu and Cd; compared to other regions, higher pollution levels were observed at sites in Oman, China, Australia, and the United Kingdom. Soil pollution by Cd, Pb, and Zn at agricultural sites was higher than that in non-agricultural sites. In addition, these heavy metal(loid)s produced a high ecological risk to soils around Cu mining sites in which the contribution of Cd, Cu, and As reached up to 46.5%, 21.7%, and 18.4%, respectively. The mean hazard indices of the eight heavy metal(loid)s were 0.209 and 0.979 for adults and children, respectively. The Monte Carlo simulation further predicted that 1.40% and 29.9% of non-carcinogenic risk values for adults and children, respectively, exceeded the safe level of 1.0. Moreover, 84.5% and 91.0% of the total cancer risk values for adults and children, respectively, exceeded the threshold of 1E-04. Arsenic was the main contributor to non-carcinogenic risk, while Cu had the highest exceedance of carcinogenic risk. Our findings indicate that the control of Cu, Cd, and As should be prioritized because of their high incidence and significant risks in soils near Cu mines. These results provide valuable inputs for policymakers in designing effective strategies for reducing the exposure of heavy metal(loid)s in this area worldwide.

Background Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

Quinclorac (3,7-dichloroquinoline-8-carboxylic acid, QNC) is a highly selective auxin herbicide that is typically applied to paddy rice fields. Its residue is a serious problem in crop rotations. In this study, Oryza sativa L. seedlings was used as a model plant to explore its biochemical response to abiotic stress caused by QNC and nZVI coexposure, as well as the interactions between QNC and nZVI treatments. Exposure to 5 and 10 mg/L QNC reduced the fresh biomass by 26.6% and 33.9%, respectively, compared to the control. The presence of 50 and 250 mg/L nZVI alleviated the QNC toxicity, but the nZVI toxicity was aggravated by the coexist of QNC. Root length was enhanced upon exposure to low or medium doses of both QNC and nZVI, whereas root length was inhibited under high-dose coexposure. Both nZVI and QNC, either alone or in combination, significantly inhibited the biosynthesis of chlorophyll, and the inhibition rate increased with elevated nZVI and QNC concentration. It was indicated that nZVI or QNC can affect the plant photosynthesis, and there was a significant interaction between the two treatments. Effects of QNC on the antioxidant response of Oryza sativa L. differed in the shoots and roots; generally, the introduction of 50 and 250 mg/L nZVI alleviated the oxidative stress (POD in shoots, SOD and MDA in roots) induced by QNC. However, 750 mg/kg nZVI seriously damaged Oryza sativa L. seedlings, which likely resulted from active iron deficiency. QNC could be removed from the culture solution by nZVI; as a result, nZVI suppressed QNC uptake by 20%–30%.

BACKGROUND Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. OBJECTIVE The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. METHODS We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. RESULTS Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. CONCLUSIONS Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.