Characterizing nonnative plants in wetlands across the conterminous United States

Nonnative plants are widely recognized as stressors to wetlands and other ecosystems. They may compete with native plant species or communities and alter ecosystem properties, which can affect ecological condition, posing challenges to resource managers. As part of the United States Environmental Protection Agency's National Wetland Condition Assessment (NWCA), we characterized the status of nonnative plants in wetlands across the conterminous United States (US). Our primary goals were to (1) document the composition of nonnative taxa at 1138 NWCA sites sampled in 2011 and (2) estimate the areal extent of wetland under stress from nonnative plants within the NWCA 2011 sampled population of ~ 25 million ha of wetland (represented by 967 sampled probability sites and the NWCA survey design). A total of 443 unique nonnative taxa were observed, encompassing a species pool adapted to diverse ecological conditions. For individual sites, the number of nonnative taxa ranged from 0 to 29, and total absolute cover of nonnatives ranged from 0 to 160%. We devised the nonnative plant indicator (NNPI) as a categorical indicator of stress (low to very high) from the collective set of nonnative plant taxa occurring at a particular location, based on a decision matrix of exceedance values for nonnative richness, relative frequency, and relative cover. Wetland area of the sampled population occurring in each NNPI category was estimated at the scale of the conterminous US and within five large ecoregions and four broad wetland types. Potential stress from nonnative plants, as indicated by the NNPI category, was low for approximately 61% (~ 15.3 million ha), moderate for about 20% (~ 5.2 million ha), high for about 10% (~ 2.48 million ha), and very high for about 9% (~ 2.2 million ha) of the wetland area in the entire sampled population. Percent of wetland area with high and very high NNPI varied by ecoregional subpopulations: greater within interior and western ecoregions (~ 29 to 87%) than within ecoregions in the eastern half of the nation (~ 11%). Among wetland type subpopulations, greater percent of wetland area with high and very high NNPI was observed for herbaceous vs. woody types and for inland vs. estuarine types. Estimates of wetland area by NNPI categories are expected to be useful to policy makers or resource managers for prioritizing management actions by identifying situations where stress from nonnative plants is most extensive. We also considered four exploratory analyses aimed at providing ecological information useful in interpreting NNPI extent results. We conducted three population-scale analyses examining ecoregional and wetland type population means for (1) the three NNPI metrics, (2) absolute cover of growth-habit groups of nonnative plants, and (3) metrics describing human-mediated disturbance. Finally, we examined ecological relationships with site-level NNPI status using a random forest (RF) analysis with NNPI as the response variable and predictor variables including ecoregion, wetland type, and a variety of characteristics describing natural vegetation structure, environment, and human-mediated disturbance.

Use of national-scale data to examine human-mediated additions of heavy metals to wetland soils of the US

Soil concentrations of 12 heavy metals that have been linked to various anthropogenic activities were measured in samples collected from the uppermost horizon in approximately 1000 wetlands across the conterminous US as part of the 2011 National Wetland Condition Assessment (NWCA). The heavy metals were silver (Ag), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), antimony (Sb), tin (Sn), vanadium (V), tungsten (W), and zinc (Zn). Using thresholds to distinguish natural background concentrations from human-mediated additions, we evaluated wetland soil heavy metal concentrations in the conterminous US and four regions using a Heavy Metal Index (HMI) that reflects human-mediated heavy metal loads based on the number of elements above expected background concentration. We also examined the individual elements to detect concentrations of heavy metals above expected background that frequently occur in wetland soils. Our data show that wetland soils of the conterminous US typically have low heavy metal loads, and that most of the measured elements occur nationally in concentrations below thresholds that relate to anthropogenic activities. However, we found that soil lead is more common in wetland soils than other measured elements, occurring nationally in 11.3% of the wetland area in concentrations above expected natural background (> 35 ppm). Our data show positive relationships between soil lead concentration and four individual landscape metrics: road density, percent impervious surface, housing unit density, and population density in a 1-km radius buffer area surrounding a site. These relationships, while evident on a national level, are strongest in the eastern US, where the highest road densities and greatest population densities occur. Because lead can be strongly bound to wetland soils in particular, maintenance of the good condition of our nation's wetlands is likely to minimize risk of lead mobilization.

Applying adaptive management and lessons learned from national assessments to address logistical challenges in the National Wetland Condition Assessment

The National Wetland Condition Assessment (NWCA) is one of a series of probability-based National Aquatic Resource Surveys (NARS) conducted by the U.S. Environmental Protection Agency (USEPA) to provide a comprehensive assessment of the condition of the Nation's waters. Randomized design and standardized training and protocols allow USEPA to analyze data that are nationally consistent and regionally relevant. Each NARS assessment was preceded by careful consideration of key logistical elements that included pre-survey planning, training, sampling logistics, and laboratory analysis. Numerous state, tribal, and contractor crews were supported across the country for each assessment; sampling and sample analyses were tracked from initiation; laboratory analyses were completed at USEPA, state, regional, and contract laboratories; and the data analyses and reporting were completed by USEPA-led workgroups, states, and contractors. The complexity and difficulty of each step offered unique challenges and provided lessons learned for each of the NARS assessments. Major logistical elements for implementing large scale assessments that are constrained by sampling period and number and duration of visits are covered in this paper. These elements include sample transport, equipment and supplies, sampling and sample tracking, information management regional technical expertise, and a sound field training program. This paper describes how lessons from previous assessments were applied to the NWCA and how new challenges faced in the NWCA were addressed and carried forward into future surveys.

Assessing the relative and attributable risk of stressors to wetland condition across the conterminous United States

We analyzed data from 967 randomly selected wetland sites across the conterminous United States (US) as part of the 2011 National Wetland Condition Assessment (NWCA) to investigate the relative and attributable risk of various stressors on wetland vegetation condition. Indicators of stress included six physical stressors (damming, ditching, filling/erosion, hardening, vegetation removal, and vegetation replacement) and two chemical stressors (soil phosphorus and heavy metals) that represent a wide range of human activities. Risk was evaluated nationally and within four aggregate ecoregions and four aggregate wetland types. Nationally, all of the stressors except soil heavy metals and phosphorus had a significant relative risk but values were always < 2 (a relative risk of two indicates that it's twice as likely to have poor vegetation condition when the stressor is present relative to when it is absent). Among the different ecoregions or wetland types, no one stressor was consistently riskier; all of the stressors were associated with poor vegetation condition in one or another of the subpopulations. Overall, hardening had the highest attributable and relative risks in the most different subpopulations. Attributable risks above 25% were observed for vegetation removal in the Coastal Plain, hardening and ditching in the West, and hardening in Estuarine Woody wetlands. Relative risks above 3 were noted for heavy metals and soil phosphorus in the Interior Plains, and vegetation removal, vegetation replacement, and damming in Estuarine Woody wetlands. Relative and attributable risk were added to the data analyses tools used in the NWCA to improve the ability of survey results to assist managers and policy makers in setting priorities based on conditions observed on the ground. These analyses provide useful information to both individual site managers and regional-national policy makers.

Quantifying the extent of human disturbance activities and anthropogenic stressors in wetlands across the conterminous United States: results from the National Wetland Condition Assessment

In 2011, the U.S. Environmental Protection Agency conducted the National Wetland Condition Assessment (NWCA) as part of the National Aquatic Resource Survey (NARS) program to determine the condition of wetlands across the 48 contiguous states of the United States (US). Sites were selected using a generalized random tessellated stratified (GRTS) probability design. We quantified the types, extent, and magnitude of human activities as indicators of potential stress on a sample of 1138 wetland sites representing a target population of 251,546 km² of wetlands in the US. We used field observations of the presence and proximity of more than 50 pre-determined types of human activity to define two types of indices that quantify human influences on wetlands. We grouped these observations into five types of human activity (classes) and summed them within and across these classes to define five metrics and an overall Human Disturbance Activity Index (HDAI). We calculated six Anthropogenic Stress Indices (ASIs) by summing human disturbance activity observations within stressor categories according to their expected effect on each of six aspects of wetland condition. Based on repeat-visit data, the precision of these metrics and indices was sufficient for regional and national assessments. Among the six categories of stress assessed nationally, the percentage of wetland area having ASI levels indicating high stress levels ranged from 10% due to filling/erosional activities to 27% due to vegetation removal activities. The proportion of wetland area with no signs of human disturbance activity (HDAI = 0) within a 140-m diameter area varied widely among the different wetland ecoregions/types we assessed. No visible human disturbance activity was evident in 70% of estuarine wetlands, but among non-estuarine wetlands, only 8% of the wetland area in the West, 15% of the Interior Plains, 22% of the Coastal Plains, and 36% of the Eastern Mountains and Upper Midwest lacked visible evidence of disturbance. The woody wetlands of the West were the most highly stressed reporting group, with more than 75% of their wetland area subject to high levels of ditching, hardening, and vegetation removal. The NWCA offers a unique opportunity to quantify the type, intensity, and extent of human activities in and around wetlands and to assess their likely stress on wetland ecological functions, physical integrity, and overall condition at regional and continental scales.

The 2011 National Wetland Condition Assessment: overview and an invitation

The first National Wetland Condition Assessment (NWCA) was conducted in 2011 by the US Environmental Protection Agency (USEPA) and its federal and state partners, using a survey design that allowed inference of results to national and regional scales. Vegetation, algae, soil, water chemistry, and hydrologic data were collected at each of 1138 locations across the conterminous United States (US). Ecological condition was assessed in relation to a disturbance gradient anchored by least disturbed (reference) and most disturbed sites identified using chemical, physical, and biological disturbance indices based on site-level data. A vegetation multimetric index (VMMI) was developed as an indicator of condition, and included four metrics: a floristic quality assessment index, relative importance of native plants, number of disturbance-tolerant plant species, and relative cover of native monocots. Potential stressors to wetland condition were identified and incorporated into two indicators of vegetation alteration, four indicators of hydrologic alteration, a soil heavy metal index, and a nonnative plant indicator and were used to quantify national and regional stressor extent, and the associated relative and attributable risk. Approximately 48 ± 6% of the national wetland area was found to be in good condition and 32 ± 6% in poor condition as defined by the VMMI. Across the conterminous US, approximately 20% of wetland area had high or very high stressor levels related to nonnative plants. Vegetation removal, hardening, and ditching stressors had the greatest extent of wetland area with high stressor levels, affecting 23-27% of the wetland area in the NWCA sampled population. The results from the 2016 NWCA will build on those from the 2011 assessment and initiate the ability to report on trends in addition to status. The data and tools produced by the NWCA can be used by others to further our knowledge of wetlands in the conterminous US.

USA-scale patterns in wetland water quality as determined from the 2011 National Wetland Condition Assessment

Water quality is a central component of ecological assessments but less well characterized in wetlands than other waterbody types. The 2011 National Wetland Condition Assessment, spanning freshwater and brackish wetlands across the conterminous USA, provided an unprecedented opportunity to examine water quality patterns across broad wetland types and geographic scales. Surface water samples were obtained from 634 (56%) of sites visited. Total nitrogen (TN), total phosphorus (TP), planktonic chlorophyll (CHLA), and specific conductance (SPCOND) ranged 4 orders of magnitude across sites and were inter-correlated. Woody versus herbaceous vegetation type was an important classifier, with herbaceous sites having standing water more often and generally higher pH, nutrients, and CHLA. Nutrient ratios spanned a range from P-limited to N-limited in most biogeographic regions, and increasing TP was associated with decreasing TN:TP ratios. Compared to national-scale data for other waterbody types (lakes, streams, marine nearshore), wetlands had generally higher TN and TP but not higher CHLA. Differences among biogeographic regions in water quality were concordant between inland wetlands and lakes, and between marine-coast wetlands and the marine nearshore. Associations of TN, TP, and CHLA to percent agriculture or natural land were stronger for the watershed scale than for smaller concentric buffer scales, suggesting that wetlands are influenced by landuse some distance away. SPCOND was related to landuse in inland wetlands but reflected seawater influence in marine-coast wetlands. Water quality exhibits the same general patterns and responses across wetlands as across other waterbody types and thus can provide a basis for ecological classification and condition assessment.

Foreword to the USEPA's National Wetland Condition Assessment Topical Collection

The Environmental Protection Agency's (EPA) National Aquatic Resources Survey (NARS) is a 5-year ongoing cycle of nationwide aquatic resource surveys which provide a report card on the condition of our nation's waters. The surveys are performed using a randomized, statistically valid design and provide statistically robust data which are used to develop the reports. These reports assess how well existing pollution prevention programs are protecting those waters, and how to better target future protection efforts. This presentation will focus on the results and uses of the 2011 National Wetland Condition Assessment (NWCA) outcomes. Some of the outcomes from the NWCA include (1) robust multi-metric indices used to evaluate condition across varying wetland types, streams, and ecoregions and (2) physical, chemical, and biological indicators of stress (risk factors) which identify the factors which contribute most to poor condition. (3) A wealth of quality-assured, statistically valid data which can be mined to pursue other questions within both regulatory and non-regulatory programs by providing a more robust look at wetland and stream condition. There are a variety of ways in which knowledge of condition can be used to better evaluate environmental states and inform decision-making. Knowledge of risk factors, for example, can be used to prioritize restoration efforts to improve the health of streams and wetlands in poor condition, as well as to identify practices to be avoided in reviewing permit applications for work in waters. The use of multi-metric plant condition indices could be useful in better identifying achieved "lift" in wetland mitigation banks, as well as providing a more robust measure of mitigation or restoration success. It is our hope to generate some seeds for future thought and discussion on ways in which the products of these NARS surveys can enhance the protection and restoration of these aquatic resources.

A novel unsupervised change detection approach based on reconstruction independent component analysis and ABC-Kmeans clustering for environmental monitoring

In this paper, I propose a new unsupervised change detection method for optical satellite imagery. The proposed technique consists of three phases. In the first stage, difference images are calculated using four different functions. Two of the functions were first used in this study. In the second stage, using Reconstruction Independent Component Analysis, this four-difference matrix is projected to one feature. In the last stage, clustering is performed. Kmeans tuned by Artificial Bee Colony (ABC-Kmeans) clustering technique has been developed and proposed by following a different strategy in the clustering phase. The effectiveness of the proposed approach was examined using two different datasets, Sardinia and Mexico. Quantitative evaluation was performed in two stages. In the first stage, proposed method was compared with different unsupervised change detection algorithms using False Alarm, Missed Alarm, Total Error, and Total Error Rate metrics which are calculated using ground truth image in dataset. In the second experimental study, the proposed approach is compared in detail with PCA-Kmeans approach, which is quite often preferred for similar studies, using the Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Universal Image Quality Index metrics. According to quantitative and qualitative analysis, proposed approach can produce quite successful results using optical remote sensing data.

Random forest-based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data

Heavy metals in the agricultural soils of reclaimed mining areas can contaminate food and endanger human health. The objective of this study is to effectively estimate the concentrations of heavy metals, such as zinc, chromium, arsenic, and lead, using hyperspectral sensor data and the random forest (RF) algorithm in the study area of Xuzhou, China. The RF's built-in feature selection ability and modeling expressive ability in heavy metal estimation of soil were explored. After the preprocessing of the spectrum obtained by an ASD (analytical spectral device) field spectrometer, the random forest algorithm was carried out to establish the estimation model based on the correlation-selected features and the full-spectrum features respectively. Results of all the different processes were compared with classical approaches, such as partial least squares (PLS) regression and support vector machine (SVM). In all the experimental results, from the perspective of models, the best estimation model for Zn (R² = 0.9061; RMSE = 6.5008) is based on the full-spectrum data of continuum removal (CR) pretreatment, and the best models for Cr (R² = 0.9110; RMSE = 4.5683), As (R² = 0.9912; RMSE = 0.5327), and Pb (R² = 0.9756; RMSE = 1.1694) are all derived from the correlation-selected features. And these best models of these heavy metals are all established by the RF method. The experiments in this paper show that random forests can make full use of the input spectral data in the estimation of four kinds of heavy metals, and the obtained models are superior to those established by traditional methods.

Standardized patient methodology in mainland China: a nationwide survey

BACKGROUND

To describe the current status of standardized patient (SP) practice in mainland China.

METHODS

We conducted a nationwide survey in 2016. One hundred and eighty-three SP educators (SPEs) responded to the questionnaire, representing 80 medical centers from 25 provinces in mainland China. All of these centers were affiliated with China Standardized Patients Practice Teaching Guidance. In the survey, we assessed the methods of SPs' recruitment, hourly wage, how SPs were used and challenges of SP role. We also compared these data among the 4 different regions in China.

RESULTS

In mainland China, the most frequent range of SPs' age was between 30 and 40 years (24.8%). The SPs were usually recruited by recommendations from the SPEs or a current SP (43.8%), as well as advertising in the hospitals (43.8%). The mean hourly wage was US$12.60 for teaching activities and US$18.82 for medical examinations. The median frequency for training SPs was 12.9 times per year. The SPs were used in areas such as internal medicine (89.6%), surgery (79.2%) and pediatrics (56.3%). The most challenging parts for the SPs were to remember all of the key points of the cases (51.9%) and portraying the emotions of the case (51.9%). Almost half of the SPs reported that, when interacting with medical students, they had difficulty in providing feedback in consistent with students' learning objectives. SPs' gender, age, rewards and scenarios playing were different significantly among the 4 geographic regions in China (P < 0.05).

CONCLUSIONS

This survey provided the reliable data on the current situation of SP application in China. SP activities have had an encouraging progress but regional development imbalance.

Curtailing Lead Aerosols: Effects of Primary Prevention on Declining Soil Lead and Children's Blood Lead in Metropolitan New Orleans

After decades of accumulation of lead aerosols in cities from additives in gasoline, in 1975 catalytic converters (which are ruined by lead) became mandatory on all new cars. By 1 January 1986 the rapid phase-down banned most lead additives. The study objective is to review temporal changes of environmental lead and children's blood lead in communities of metropolitan New Orleans. In 2001, a soil lead survey of 287 census tracts of metropolitan New Orleans was completed. In August-September 2005 Hurricanes Katrina and Rita storm surges flooded parts of the city with sediment-loaded water. In April-June 2006, 46/287 (16%) of the original census tracts were selected for resurvey. A third survey of 44/46 (15%) census tracts was completed in 2017. The census tract median soil lead and children's median blood lead decreased across surveys in both flooded and unflooded areas. By curtailing a major urban source of lead aerosols, children's lead exposure diminished, lead loading of soil decreased, and topsoil lead declined. Curtailing lead aerosols is essential for primary prevention. For the sake of children's and ultimately societal health and welfare, the long-term habitability of cities requires terminating all remaining lead aerosols and cleanup of legacy-lead that persists in older inner-city communities.

Distribution, source identification, and ecological-health risks of potentially toxic elements (PTEs) in soil of thallium mine area (southwestern Guizhou, China)

The exploitation of thallium (Tl) resources through mining poses a significant threat to ecological systems and human health due to its high toxicity and ready assimilation by human body. We report the first assessment of the pollution, spatial distribution, source, and ecological-health risks of potentially toxic elements (PTEs) in Tl mining area of southwest Guizhou, China. Spatial distribution maps for PTEs were visualized by ArcGIS to identify their distribution trends. We use the enrichment factor (EF), correlation analysis, and principal component analysis to identify likely sources of seven PTEs mining area. The wider risk assessment was evaluated using the geoaccumulation index (I_geo), potential ecological risk index (RI), human non-carcinogenic risk (HI), and carcinogenic risk (CR). The results revealed the PTEs content in the study area identifies direct mining, metal production, and domestic pollution sources. In addition, the distribution of PTEs was also affected by the topography, rain water leaching, and river dispersals. The main elements of concern are Tl and As, while Cd, Cr, Cu, Pb, and Zn do not show significant enrichment in the area despite associations with the ore deposit. Risk assessment identifies strong pollution and ecological risks and poses unacceptable human health risks to local residents, especially for children. The ecological risk in the study is identified to be predominantly from Tl (74.32%), followed by As (8.57%) and Cd (7.32%). The contribution of PTEs to the non-carcinogenic risk of humans in the study area is exclusively from As and Tl, while the carcinogenic risk is dominated by As, and the other elements pose no significant risk to human health.

Quantitative design and analysis of marine environmental monitoring networks in coastal waters of China

The quality of seawater needs to be continuously monitored due to its effect on human life and natural ecosystems. However, the balance of the extent, spatial pattern and maintenance costs of marine environmental monitoring remains a challenging issue which is crucial for decision-makers. The main contribution of this work suggests taking advantage of two minimization criteria (TMC: integrating minimization of Kriging variance and minimization of relative error at a given confidence level) to improve the design and optimization of a marine environmental monitoring network. To achieve this purpose, the spatial simulated annealing (SSA) method is applied to identify the best locations for monitoring network optimization. For the case study, phosphate (PO₄) is used as an indicator to characterize the seawater quality in northern coastal waters of Zhejiang Province, China. The 122 existing sites have redundancies (about 78 sites) that can be effectively identified and removed to reduce costs with the given relative error (less than 10%) and confidence level (95%). Some new sites can be added and adjusted to improve the quality of costal environmental monitoring based on quantitative analysis. In addition, the relationship between the number of the monitoring sites and monitoring precision is analyzed. The results suggest that the present method using TMC can provide a scientific basis for marine environmental monitoring and management.

Examining the spatially varying effects of factors on PM2.5 concentrations in Chinese cities using geographically weighted regression modeling

Whilst numerous studies have explored the spatial patterns and underlying causes of PM_2.5, little attention has been paid to the spatial heterogeneity of the factors affecting PM_2.5. In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM_2.5 in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM_2.5 concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM_2.5 in 95.60% of the cities and it correlated negatively with PM_2.5 in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM_2.5. The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM_2.5, revealing that increasing urban fragmentation was conducive to PM_2.5 reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM_2.5 governances.

Temporal trends in organophosphorus pesticides use and concentrations in river water in Japan, and risk assessment

We reviewed organophosphorus pesticide use in Japan between 1982 and 2016 using data from the National Institute of Environmental Studies. Organophosphorus pesticide concentrations in river water throughout Japan were taken from the literature, and risk assessments were performed for some organophosphorus pesticides based on risk quotients and hazard quotients. Assessments were performed for 20 common pesticides, including insecticides, fungicides, and herbicides. The amounts used decreased in the order: insecticides > herbicides > fungicides. Organophosphorus insecticide and fungicide use have decreased over the last four decades, but organophosphorus herbicide use has increased. During this period, annual organophosphorus pesticide use was the highest for chlorpyrifos (105,263 tons/year) and the lowest for glyphosate-sodium (8 tons/year). The ecotoxicological risk assessment indicated that diazinon and fenitrothion posed strong risks to the Japanese aquatic environment, and chlorpyrifos and malathion have moderate risks. None of the pesticides that were assessed posed significant risks to humans. Continued use of organophosphorus pesticides in Japan may cause strong risks to aquatic environments. These risks should be reassessed periodically.

Improved enrichment factor calculations through principal component analysis: Examples from soils near breccia pipe uranium mines, Arizona, USA

The enrichment factor (EF) is a widely used metric for determining how much the presence of an element in a sampling media has increased relative to average natural abundance because of human activity. Calculation of an EF requires the selection of both a background composition and a reference element, choices that can strongly influence the result of the calculation. Here, it is shown how carefully applied, classical principal component analysis (PCA) examined via biplots can guide the selections of background compositions and reference elements. Elemental data were treated using the centered log ratio (CLR) transformation, and multiple subsets of major and trace elements were examined to gain different perspectives. The methodology was applied to a dataset of elemental soil concentrations from around breccia pipe uranium mines in Arizona, U.S.A., with most samples collected via incremental sampling methodology. Storage of ore at the surface creates the potential for wind dispersal of ore-derived material. Uranium was found to be the best individual tracer of dispersal of ore-derived material to nearby soils, with EF values up to 75. Sulfur, As, Mo, and Cu were also enriched but to lesser degrees. The results demonstrate several practical benefits of a PCA in these situations: (1) the ability to identify one or more elements best suited to distinguish a specific source of enrichment from background composition; (2) understanding how background compositions vary within and between sites; (3) identification of samples containing enriched or anthropogenic materials based upon their integrated, multi-element composition. Calculating the most representative EF values is useful for numerical assessment of enrichment, whether anthropogenic or natural. As shown here, however, the PCA and biplot method provide a visual approach that integrates information from all elements for a given subset of data in a manner that yields geochemical insights beyond the power of the EF.

A practical approach to improve the statistical performance of surface water monitoring networks

The representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU's Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying the uncertainties in the status class estimation. We estimated the temporal and spatial variance components, as well as the effect of sampling allocation to the precision and confidence of chlorophyll-a and total phosphorus. Our results suggest that almost 70% of the lake and coastal waterbodies, and 27% of the river waterbodies, were classified without sufficient confidence in these variables. On the other hand, many of the waterbodies produced unnecessary precise metric means. Thus, reallocation of sampling effort is needed. Our results show that, even though the studied variables are among the most monitored status metrics, the unexplained variation is still high. Combining multiple data sets and using fixed covariates would improve the modeling performance. Our study highlights that ongoing monitoring programs should be evaluated more systematically, and the information from the statistical uncertainty analysis should be brought concretely to the decision-making process.

Changes in Major Global River Discharges Directed into the Ocean

Under the influence of global climate change, the discharges of major global rivers directed into the ocean have undergone significant changes. To study the trends and causes in discharge variation, we selected 40 large rivers and analyzed their annual discharges near their estuaries from 1960 to 2010. The method of runoff variation attribution analysis based on the Budyko hypothesis for large-scale basins was developed, in which influencing factors of human activities and glacial melting factors were added to the formula. The contribution rate of climate factors and human activities to changes in discharge were quantitatively identified. Climatic factors include precipitation, evapotranspiration and glacial melting. Human activity factors include underlying surface and artificial water transfer. The contribution rate is determined by the elastic coefficient, which is obtained by the ratio of change rate of each factor and the change rate of runoff. The results indicated that the discharges predominantly showed downward trends with a few upward trends. Rivers in North America and Africa showed downward trends, and those in Europe principally showed upward trends. Climate was the main influencing factor of discharges changes, and only approximately 25% of river discharges were greatly affected by human activities. River discharges in 75% of the basins which mainly contains subtropical monsoon humid climate and savanna climate zones showed upward trends. In the four basins which are mainly contains tropical rainforest climate and tropical monsoon climate, they all showed downward trends. The trend of discharges in the temperate monsoon climate, temperate continental climate, and temperate maritime climate cannot be accurately judged because of irregular variation. The discharges in the mid-high latitudinal zones predominantly showed upward trends, while those in the mid-low latitudinal zones with the influence of human activities showed downward trends.

Heavy metals inter-annual variability and distribution in the Yangtze River estuary sediment, China

A better understanding of the relationships between heavy metal pollution and sediment sources in different aquatic environments of estuary is needed. The purpose of this paper is to evaluate the levels and spatial distribution of heavy metals (Cu, Zn, Pb, Cd, Hg, and As) of surface sediments collected from 20 stations in the Yangtze River estuary from 2012 to 2016. The results showed that an overall decreasing trend in the concentration of these six types of heavy metals with distance from the shoreline. Typically, there was also a high concentration of heavy metals in muddy areas. Principle component analysis (PCA) and Factor analysis (FA) detected major groups of elements in which three distinct clusters of pollutants and recognized the sources of metals and validate the temporal and spatial variations that are affected by natural and anthropogenic sources linked to their seasonality.

An overview of worldwide and regional time trends in total mercury levels in human blood and breast milk from 1966 to 2015 and their associations with health effects

BACKGROUND

Mercury is a pollutant of global concern. To protect human health and environment from mercury pollution, the Minamata Convention on mercury entered into force in 2017.

OBJECTIVES

To support a future effectiveness evaluation of the convention, this study assesses worldwide and regional time trends of total mercury levels in human blood and breast milk across different population sub-groups in the last half-century prior to entry-into-force of the Minamata Convention. This study also provides an overview of the epidemiological literature showing evidence of associations between mercury exposure (in terms of total mercury levels in whole blood, cord blood, and breast milk) and human health.

METHODS

We searched electronic databases to identify articles published prior to June 14, 2017 and reported total mercury levels in any of three biological matrices (whole blood, cord blood, or breast milk) and/or associations with human health. Temporal trends of total mercury levels in the selected biological matrices across different population sub-groups were estimated using a linear fit of the log-transformed data. In parallel, statistical methods were employed to assess any possible effect of sources of inhomogeneity (i.e. study and population characteristics such as age, sex, ethnicity, source of exposure, sampling period, and geographical region) in the collected studies. Furthermore, a summary of significant and relevant associations between mercury exposure and human health conditions in children and adults was prepared.

FINDINGS

We found significant declines in total mercury levels in whole blood, cord blood, and breast milk between 1966 and 2015. A regional overview of total mercury levels in whole blood, cord blood, and breast milk suggests the highest levels in South America, followed by Africa or Asia whereas the population groups from Europe or North America displayed the lowest levels of total mercury in the selected biological matrices. We observed conclusive consistent associations of mercury exposure with selected health conditions, especially neurodevelopment and neurotoxicity in children and adults. For several other health conditions, reported findings in the collected studies do not support conclusive associations. We also found that several studies demonstrated significant associations between mercury exposure below the USEPA reference level and various health conditions.

CONCLUSIONS

This study provides a worldwide and regional overview of trends in total mercury levels in human blood and breast milk and associated health risks prior to entry-into-force of the Minamata Convention and calls for further epidemiological investigations from across the globe to fully understand the health implications of mercury exposure.

Environmental status of Israel's Mediterranean coastal waters: Setting reference conditions and thresholds for nutrients, chlorophyll-a and suspended particulate matter

Criteria for eutrophication related parameters to achieve and preserve good environmental status (GES) of the oligotrophic Israeli Mediterranean coast were proposed for nutrients, chlorophyll-a (Chl-a) and suspended particulate matter (SPM) concentrations. The criteria were derived from current conditions, the best choice for the area that has undergone large and irreversible ecological changes compared to the pristine background. A five-year data set (2010-2014, ca. 800 data points) was analyzed using statistical methods and best professional judgement. The coastal waters were divided into four provinces, data gaps were identified, and seasonal reference and threshold values for each province determined as the median and 1.5 times the median, respectively. Application of the derived criteria to data up to 2016 showed the coastal waters to be mainly in GES, with a few exceptions. Simplification of the proposed criteria for environmental management was addressed as well.

The impacts of urbanization on fine particulate matter (PM2.5) concentrations: Empirical evidence from 135 countries worldwide

Few attempts have been made to systematically investigate the impacts of urbanization on PM_2.5 concentrations in countries at different stages of economic development. In this study, a broad concept of urbanization that considers the transformations in the urban economy and the transport sector induced by urbanization is proposed to investigate the influence of urbanization on national PM_2.5 concentrations for underdeveloped, developing and developed countries during 1998-2014. The results indicate that urbanization has a significant relationship with PM_2.5 concentrations, but the magnitude of its influence varies among groups of countries with different development levels. First, the positive response of PM_2.5 concentrations to increased urbanization and transport-related emissions in underdeveloped countries are noticeably stronger than that in developing and developed countries. Second, for developing countries, urbanization, transport-related emissions and industrialization all have a significant positive effect on national PM_2.5 concentrations increase, although their impacts are unexpectedly smaller than those in the other groups of countries. Finally, increasing urbanization and the decrease in CO₂ emissions from manufacturing industry appear to reduce national average PM_2.5 concentrations in developed countries, while the decrease in transport-related CO₂ emission is likely to cause the rise in national average PM_2.5 concentrations.

Analysis on urban densification dynamics and future modes in southeastern Wisconsin, USA

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.

Effects of offshore oil exploration and development in the Alaskan Beaufort Sea: Long-term patterns of hydrocarbons in sediments

The United States Bureau of Ocean Energy Management (BOEM) has sponsored 4 major monitoring projects in the oil and gas development area of the Alaskan Beaufort Sea since the 1980s, the last being the Arctic Nearshore Impact Monitoring in the Development Area III (ANIMIDA III) Project (2014-2017). These studies were conducted to better understand the physical, chemical, and biological environments and how oil and gas activities may impact them. This paper focuses on monitoring sediment hydrocarbon chemistry. The projects included measuring polycyclic aromatic hydrocarbons (PAHs), n-alkanes and isoprenoids (SHCs), and sterane/triterpene (S/T) geochemical biomarkers and determining their distribution, possible sources, and environmental significance in the sediments of the Beaufort Sea and rivers emptying into it. Concentrations of hydrocarbons in sediments were variable on both spatial and temporal scales; surface sediment concentrations of total PAHs (TPAHs), the class of hydrocarbons of greatest environmental interest, averaged between 300 and 700 μg/kg in different years of monitoring between 1985 and 2015. The concentrations were similar to those measured in other marine regions of Alaska where oil activities have not occurred. Sediment TPAH concentrations were below sediment quality guidelines values, indicating a low risk of harm to benthic marine communities. The hydrocarbons in the Beaufort Sea sediments are primarily from non-oil petrogenic and biogenic sources, with small amounts of pyrogenic hydrocarbons. Most of the hydrocarbons are carried to the Beaufort Sea in coastal erosion and river inputs of hydrocarbon-rich materials, such as peat and shale. The majority of the Beaufort Sea Development Area, including near production facilities, contains uncontaminated sediments with only a few small areas near (<100 m) some exploratory wells where petroleum hydrocarbon concentrations are above regional background. Integr Environ Assess Manag 2019;15:224-236. © 2019 SETAC.

Applying linear and nonlinear models for the estimation of particulate matter variability

In this study, data collected from an urban air quality monitoring network are being used for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations. The examined methods are the Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model. These schemes utilize daily PM₁₀ and PM_2.5 concentrations collected from five and three air quality monitoring sites respectively. In order to obtain the resulted estimations, the leave-one-out cross-validation methodology is used for all methods. The evaluation of their predictive ability is performed by using a combination of difference and correlation statistical measures, scatter plots and statistical tests. The results indicate the usefulness of FFNNs as they are found to be statistically significantly superior for modelling the particulate matter spatial variability. The model performance statistics show that in most cases the error values are considerably lower for the FFNN model. Additionally, the rank and Wilcoxon rank tests reveal that the null hypothesis for equal predictive accuracy is rejected for the majority of monitoring sites and schemes (values lower than the critical t-value). According to the comparison results, the FFNN model is selected for forecasting air quality limit exceedances set by the European Union and World Health Organization air quality standards. For two monitoring sites in which the largest number of exceedances occurred, the probability of detection is high while the probability of false detection is very low, further establishing the neural networks' predictive ability.

Effects of offshore oil exploration and development in the Alaskan Beaufort Sea: A three-decade record for sediment metals

Impacts from oil exploration, development, and production in the Beaufort Sea, Alaska, USA are assessed using concentrations of metals in sediments collected during 2014 to 2015, combined with a large data set for 1985 to 2006. Concentrations of 7 (1980s) or 17 (1999-2015) metals in 423 surface sediments from 134 stations, plus 563 samples from 30 cores were highly variable, primarily as a function of sediment granulometry with naturally greater metals concentrations in fine-grained, Al-rich sediment. Metals versus Al correlation plots were used to normalize metals concentrations and identify values significantly above background. Barium, Cr, Cu, Hg, and Pb concentrations were above background, but variable, within 250 m of some offshore sites where drilling occurred between 1981 and 2001; these areas totaled <6 km² of 11 000 km² in the total lease area. Random and fixed sampling along the coastal Beaufort Sea from 1985 to 2015 yielded 40 positive anomalies for metals in surface sediments (∼0.8% of 5082 data points). About 85% of the anomalies were from developed areas. Half the anomalies were for the 5 metals found enhanced near drilling sites. No metals concentrations, except As, exceeded accepted sediment quality criteria. Interannual shifts in metals values for surface sediments at inner shelf sites were common and linked to storm-induced transitions in granulometry; however, metal-to-Al ratios were uniform during these shifts. Sediment cores generally recorded centuries of background values, except for As, Fe, and Mn. These 3 metals were naturally enriched in sediments from deeper water (>100 m) via diagenetic remobilization at sediment depths of 5 to 15 cm, upward diffusion, and precipitation in surface oxic layers. Minimal evidence for anthropogenic inputs of metals, except near some exploratory drilling sites, is consistent with extraction of most oil from land or barrier islands in the Alaskan Arctic and restricted offshore activity to date. Integr Environ Assess Manag 2019;15:209-223. © 2018 SETAC.

Particulate matter pollution in Chinese cities: Areal-temporal variations and their relationships with meteorological conditions (2015-2017)

As the second largest economy in the world, China experiences severe particulate matter (PM) pollution in many of its cities. Meteorological factors are critical in determining both areal and temporal variations in PM pollution levels; understanding these factors and their interactions is critical for accurate forecasting, comprehensive analysis, and effective reduction of this pollution. This study analyzed areal and temporal variations in concentrations of PM_2.5, PM₁₀, and PM_coarse (PM₁₀ - PM_2.5) and PM_2.5 to PM₁₀ ratios (PM_2.5/PM₁₀) and their relationships with meteorological conditions in 366 Chinese cities from January 1, 2015 to December 31, 2017. On the national scale, PM_2.5 and PM₁₀ decreased from 48 to 42 μg m^-³ and from 88 to 84 μg m^-³, respectively, and the annual mean concentrations were 45 μg m^-³ (PM_2.5) and 84 μg m^-³ (PM₁₀) during the time period (2015-2017). In most regions, largest PM concentrations occurred in winter. However, in northern China, in spring PM_coarse concentrations were highest due to dust. The PM_2.5/PM₁₀ ratio was higher in southern than in northern China. There were large regional disparities in PM diurnal variations. Generally, PM concentrations were negatively correlated with precipitation, relative humidity, air temperature, and wind speed, but were positively correlated with surface pressure. The sunshine duration showed negative and positive impacts on PM in northern and southern cities, respectively. Meteorological factors impacted particulates of different size differently in different regions and over different periods of time.

Airborne Alternaria and Cladosporium fungal spores in Europe: Forecasting possibilities and relationships with meteorological parameters

Airborne fungal spores are prevalent components of bioaerosols with a large impact on ecology, economy and health. Their major socioeconomic effects could be reduced by accurate and timely prediction of airborne spore concentrations. The main aim of this study was to create and evaluate models of Alternaria and Cladosporium spore concentrations based on data on a continental scale. Additional goals included assessment of the level of generalization of the models spatially and description of the main meteorological factors influencing fungal spore concentrations. Aerobiological monitoring was carried out at 18 sites in six countries across Europe over 3 to 21 years depending on site. Quantile random forest modelling was used to predict spore concentrations. Generalization of the Alternaria and Cladosporium models was tested using (i) one model for all the sites, (ii) models for groups of sites, and (iii) models for individual sites. The study revealed the possibility of reliable prediction of fungal spore levels using gridded meteorological data. The classification models also showed the capacity for providing larger scale predictions of fungal spore concentrations. Regression models were distinctly less accurate than classification models due to several factors, including measurement errors and distinct day-to-day changes of concentrations. Temperature and vapour pressure proved to be the most important variables in the regression and classification models of Alternaria and Cladosporium spore concentrations. Accurate and operational daily-scale predictive models of bioaerosol abundances contribute to the assessment and evaluation of relevant exposure and consequently more timely and efficient management of phytopathogenic and of human allergic diseases.

Measurement error in a multi-level analysis of air pollution and health: a simulation study

BACKGROUND

Spatio-temporal models are increasingly being used to predict exposure to ambient outdoor air pollution at high spatial resolution for inclusion in epidemiological analyses of air pollution and health. Measurement error in these predictions can nevertheless have impacts on health effect estimation. Using statistical simulation we aim to investigate the effects of such error within a multi-level model analysis of long and short-term pollutant exposure and health.

METHODS

Our study was based on a theoretical sample of 1000 geographical sites within Greater London. Simulations of "true" site-specific daily mean and 5-year mean NO₂ and PM₁₀ concentrations, incorporating both temporal variation and spatial covariance, were informed by an analysis of daily measurements over the period 2009-2013 from fixed location urban background monitors in the London area. In the context of a multi-level single-pollutant Poisson regression analysis of mortality, we investigated scenarios in which we specified: the Pearson correlation between modelled and "true" data and the ratio of their variances (model versus "true") and assumed these parameters were the same spatially and temporally.

RESULTS

In general, health effect estimates associated with both long and short-term exposure were biased towards the null with the level of bias increasing to over 60% as the correlation coefficient decreased from 0.9 to 0.5 and the variance ratio increased from 0.5 to 2. However, for a combination of high correlation (0.9) and small variance ratio (0.5) non-trivial bias (> 25%) away from the null was observed. Standard errors of health effect estimates, though unaffected by changes in the correlation coefficient, appeared to be attenuated for variance ratios > 1 but inflated for variance ratios < 1.

CONCLUSION

While our findings suggest that in most cases modelling errors result in attenuation of the effect estimate towards the null, in some situations a non-trivial bias away from the null may occur. The magnitude and direction of bias appears to depend on the relationship between modelled and "true" data in terms of their correlation and the ratio of their variances. These factors should be taken into account when assessing the validity of modelled air pollution predictions for use in complex epidemiological models.

Technical quality of fauna monitoring programs in the environmental impact assessments of large mining projects in southeastern Brazil

Biodiversity monitoring is a key element of impact assessment follow-up activities, as it has the potential to generate relevant information about the actual impacts of approved projects on the environment. However, the effectiveness of such monitoring programs depends on issues such as technical quality. The extent to which this issue actually affects biodiversity monitoring is unclear. This knowledge gap was addressed in this study, whose main objective was to analyze the technical quality of fauna monitoring, using empirical data from large-scale mining enterprises in the state of Minas Gerais in southeastern Brazil. More specifically, this study aimed at analyzing: 1) whether license conditions related to fauna monitoring programs were being met by mining companies; 2) the extent to which fauna monitoring programs met a set of technical quality criteria; 3) whether there were significant differences among taxonomic groups; and, finally, 4) how fauna monitoring programs can be made more meaningful to decision-makers. A total of 236 fauna monitoring reports were analyzed. Findings indicated that, while companies complied with all license conditions, their fauna programs met, on average, 32% of the desirable technical requirements, and there were no significant differences among taxonomic groups. The main technical quality gaps were found to be lack of driving questions, hypothesis-testing and conceptual models, as well as lack of comparisons between control and impacted areas. Overall, findings indicated that the data generated in such programs have very limited value to decision-makers as they do not shed sufficient light on the actual impacts of mining activities on biodiversity. The study discusses a number of barriers to more meaningful fauna monitoring programs, and highlights the urgent need for revising current Terms of Reference.

Significantly decreasing harmful algal blooms in China seas in the early 21st century

Harmful algal blooms (HABs) are one of primary worldwide environmental problems with severe consequences for aquatic ecosystems, human health, marine fisheries and local economy. During the past few decades, coastal waters of China Seas have experienced a significant increase in the occurrence of the HAB events, which is common across the vast majority of coastal waters of the world. Here we report the absence of the widespread increase over the early 21st century in China Seas. Both frequency and coverage area of annual HAB events have decreased at statistically significant rates for the 2000-2017 period. Despite the multiple factors determining the outbreak of HABs, the improvement of water quality in the marginal sea off China and changes in the sea surface temperature in the early 21st century may play an important role in the decrease in the HABs.

Using multivariate statistical analyses to identify and evaluate the main sources of contamination in a polluted river near to the Liaodong Bay in Northeast China

Using multivariate statistical analysis, the study evaluated anthropogenic sources of river water contamination and their relationships with river water quality in the Haicheng River basin near to the Liaodong Bay in Northeast China. The results showed that nitrogen (N) and phosphorous (P) were identified as the main pollutants in the river water by factor analysis. Human population and elevational gradient were all significantly correlated with N, P, and other water quality variables in correlation analysis and explained chemical oxygen demand (COD), N, and P variables from 23.9% (TN) to 53.1% (NH₃⁺-N) of the total variances in regression analysis, indicating that population and its distribution were all responsible for river contaminations, especially for COD, N, and P contaminations. The excessive applications of fertilizers and pesticides were all positively correlated with nitrogen variables and nitrogen pollution factor in correlation analysis, suggesting that agricultural activities were contributed to the river nitrogen pollution. Due to inadequate or lack wastewater treatment facilities, huge amounts of domestic sewage and industrial effluents were released into the river, becoming the predominant anthropogenic sources for the river water deterioration of COD, N, and P. Multivariate statistical analysis provided useful tools to correlate sources of contamination with water quality data. This approach will provide a better management for river pollution control in a human-driven river ecosystem.

Quantification is more than counting: Actions required to accurately quantify and report isolated marine microplastics

Research on marine microplastics continues to increase in popularity, with a large number of studies being published every year. However, with this plethora of research comes the need for a standardised approach to quantification and analysis procedures in order to produce comparative assessments. Using data collected from neuston nets in 2016, parameters for quantifying microplastics were compared. Surface area was the most accurate parameter to describe plastic size and should be used to describe plastic quantity (per km² or m³), alongside abundance. Of the two most commonly used methods for calculating plastic concentration (flowmeter and ship's log), ship's log provided consistently smaller abundances, with the exception of one sample, calling for a standardisation in the techniques and measurements used to quantify floating microplastics.

Applying multivariate statistics for identification of groundwater resources and qualities in NW Turkey

This study, performed in Çanakkale-Ezine in NW of Turkey, analyzes the physicochemical properties of 37 groundwater wells. These 37 wells were chosen to represent each geological unit in the study area. The main purpose of the study and its contribution to the literature is to produce information about the resources and availability of groundwater by using multivariate statistical methods and lithology. For determination hydrochemical facies of groundwater, Piper trilinear diagram was used. Gibbs diagram was applied for determining the mechanism of groundwater chemistry and diagram showed that the interaction of rock-water is more dominant in the study area. Multivariate statistics were applied to physicochemical properties for identification origins of waters. According to the Piper diagram, 16 of the wells were identified as Ca-HCO₃ type, 13 of them as Ca-Cl type, 5 of them as mixed Ca-Mg-Cl type, 2 of them as Na-Cl type, and 1 as Ca-Na-HCO₃ type. In the study with the purpose of determining the resources of groundwater, the physicochemical properties of the wells are analyzed with hierarchical cluster (HCA) and non-hierarchical cluster (K-means) methods, and the resources are associated with the lithology based on these methods. A total of 37 wells are divided into five different clusters through the HCA method. Further, for the interpretation of the resources of the groundwater, the facies of the waters on the Piper diagram are evaluated based on the five clusters generated through the HCA method and on the lithology. In the study, the results obtained from the K-means method are not significant and in line with the lithology for the interpretation of the resources of the groundwater. In conclusion, this study with limited dataset reveals that using HCA method is very effective to identify the origins of groundwater and present the association with lithology.

Statistical assessment of nonpoint source pollution in agricultural watersheds in the Lower Grand River watershed, MO, USA

The water quality in many Midwestern streams and lakes is negatively impacted by agricultural activities. Although the agricultural inputs that degrade water quality are well known, the impact of these inputs varies as a function of geologic and topographic parameters. To better understand how a range of land use, geologic, and topographic factors affect water quality in Midwestern watersheds, we sampled surface water quality parameters, including nitrate, phosphate, dissolved oxygen, turbidity, bacteria, pH, specific conductance, temperature, and biotic index (BI) in 35 independent sub-watersheds within the Lower Grand River Watershed in northern Missouri. For each sub-watershed, the land use/land cover, soil texture, depth to bedrock, depth to the water table, recent precipitation area, total stream length, watershed shape/relief ratio, topographic complexity, mean elevation, and slope were determined. Water quality sampling was conducted twice: in the spring and in the late summer/early fall. A pairwise comparison of water quality parameters acquired in the fall and spring showed that each of these factors varies considerably with season, suggesting that the timing is critical when comparing water quality indicators. Correlation analysis between water quality indicators and watershed characteristics revealed that both geologic and land use characteristics correlated significantly with water quality parameters. The water quality index had the highest correlation with the biotic index during the spring, implying that the lower water quality conditions observed in the spring might be more representative of the longer-term water quality conditions in these watersheds than the higher quality conditions observed in the fall. An assessment of macroinvertebrates indicated that the biotic index was primarily influenced by nutrient loading due to excessive amounts of phosphorus (P) and nitrogen (N) discharge from agricultural land uses. The PCA analysis found a correlation between turbidity, E. coli, and BI, suggesting that livestock grazing may adversely affect the water quality in this watershed. Moreover, this analysis found that N, P, and SC contribute greatly to the observed water quality variability. The results of this study can be used to improve decision-making strategies to improve water quality for the entire river basin.

Description of exposure profiles for seven environmental chemicals in a US population using recursive partition mixture modeling (RPMM)

Biomonitoring studies have shown that humans are exposed to numerous environmental chemicals. Previous work provides limited insights into the dynamic relationship between different chemicals within a population. The objective of this study is to develop an analytical method identifying exposure profiles of seven common environmental chemicals and determine how exposure profiles differ by sociodemographic groups and National Health and Nutrition Examination Survey (NHANES) 2003-2012 cycle year. We used recursive partition mixture modeling (RPMM) to define classes of the population with similar exposure profiles of lead, cadmium, 2,4-dichlorophenol, 2,5-dichlorophenol, bisphenol A (BPA), triclosan, and benzophenone-3 in individuals aged ≥6 years. Additionally, quasibinomial logistic regression was used to examine the association between each class and selected demographic characteristics. Eight exposure profiles were identified. Individuals who clustered together and had the highest chemical exposures were more likely to be older, to be Non-Hispanic Black (NHB) or Other Hispanic (OH), more likely to live below the poverty line, more likely to be male, and more likely to have participated in the earlier NHANES cycle (2003-2004). The developed method described the dynamic relationship between chemicals and shows that this relationship is different for subpopulations based on their sociodemographic characteristics.

The association between ambient air pollution and birth defects in four cities in Hunan province, China, from 2014 to 2016

This study was performed to assess whether air pollution was positively associated with birth defects and if a specific pregnancy stage played a role. This was a population-based case-control study comprising 153,822 perinatal births in four cities located in Hunan province, China, during the period 2014 to 2016. Exposure to SO2, NO2, and PM10 in each pregnant woman in the first 3 months before pregnancy, and in the first and third trimester was assessed. The risk of birth defects related to SO2 in the first 3 months before pregnancy was between 1.191 and 1.566. In the first trimester stage the risk was between 1.104 and 1.348. The risk of birth defects related to NO2 before pregnancy was 1.285 (95%CI: 1.180-1.399), in the first trimester stage the risk was between 1.280 (95%CI: 1.197-1.368) and 1.380 (95%CI: 1.293-1.473). In the third month before delivery the risk was 1.484 (95%CI: 1.366-1.613). The risk of birth defects related to PM10 in the first month of pregnancy was 1.098 (95%CI: 1.057-1.140), and in the third month before delivery the risk was 1.296 (95%CI: 1.222-1.375). SO2 had a greater effect on the prophase of pregnancy, while NO2 and PM10 had an effect in the late third trimester.

Risk prediction system for dengue transmission based on high resolution weather data

BACKGROUND

Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction.

METHODS AND FINDINGS

We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM's Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach.

CONCLUSIONS

We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.

Tracking Declines in Mercury Exposure in the New York City Adult Population, 2004-2014

Mercury is a toxic metal that can be measured in human blood and urine. Population-based biomonitoring from 2004 guided New York City (NYC) Department of Health and Mental Hygiene (DOHMH) efforts to reduce exposures by educating the public about risks and benefits of fish consumption-a predominant source of exposure in the general population-and removing mercury-containing skin-lightening creams and other consumer products from the marketplace. We describe changes in exposures over the past decade in relation to these local public health actions and in the context of national changes by comparing mercury concentrations measured in blood (1201 specimens) and urine (1408 specimens) from the NYC Health and Nutrition Examination Survey (NYC HANES) 2013-2014 with measurements from NYC HANES 2004 and National Health and Nutrition Examination Surveys (NHANES) 2003-2004 and 2013-2014. We found that NYC adult blood and urine geometric mean mercury concentrations decreased 46% and 45%, respectively. Adult New Yorkers with blood mercury concentration ≥ 5 μg/L (the New York State reportable level) declined from 24.8% (95% CL = 22.2%, 27.7%) to 12.0% (95% CL = 10.1%, 14.3%). The decline in blood mercury in NYC was greater than the national decline, while the decline in urine mercury was similar. As in 2004, Asian New Yorkers had higher blood mercury concentrations than other racial/ethnic groups. Foreign-born adults of East or Southeast Asian origin had the highest prevalence of reportable levels (29.7%; 95% CL = 21.0%, 40.1%) across sociodemographic groups, and Asians generally were the most frequent fish consumers, eating on average 11 fish meals in the past month compared with 7 among other groups (p < 0.001). Fish consumption patterns were similar over time, and fish continues to be consumed more frequently in NYC than nationwide (24.7% of NYC adults ate fish ten or more times in the past 30 days vs. 14.7% nationally, p < 0.001). The findings are consistent with the hypothesis that blood mercury levels have declined in part because of local and national efforts to promote consumption of lower mercury fish. Local NYC efforts may have accelerated the reduction in exposure. Having "silver-colored fillings" on five or more teeth was associated with the highest 95th percentile for urine mercury (4.06 μg/L; 95% CL = 3.1, 5.9). An estimated 5.5% of the adult population (95% CL = 4.3%, 7.0%) reported using a skin-lightening cream in the past 30 days, but there was little evidence that use was associated with elevated urine mercury in 2013-14.

Multiple water source use in rural Vanuatu: are households choosing the safest option for drinking?

Households in low- and middle-income countries commonly make use of multiple water sources. However, it remains unclear to what extent their drinking water choices are optimal from a health perspective. This matched cohort study across 10 islands in Vanuatu examined communities with both a groundwater and rainwater source to determine whether their preferred drinking option was the safest in microbial terms. In communities that preferred to drink rainwater, 56.5% of rainwater sources had 'high-risk' or 'very high risk' Escherichia coli contamination (> 10 MPN/100 mL) compared with 26.1% of groundwater sources (p = 0.092). Moreover, a preference for drinking rainwater was significantly associated with rainwater sources having 'high-risk' or 'very high risk' levels of E. coli contamination (p = 0.045). Results show that communities do not necessarily choose the safest water source for drinking. Findings also highlight the need to bolster local capacity to manage water quality risks and for Sustainable Development Goal monitoring to distinguish between protected and unprotected rainwater tanks.

Characteristic contaminants in snowpack and snowmelt surface runoff from different functional areas in Beijing, China

Characteristics of physicochemical parameters, dissolved-phase heavy metals, and polycyclic aromatic hydrocarbons (PAHs) were investigated for 68 urban snowmelt surface runoff and snowpack samples collected from five different functional areas in Beijing, including a business area (BA), a cultural and educational area (CEA), a garden area (GA), a residential area (RA), and a roadside area (RSA). Both snowmelt surface runoff and snowpack were significantly polluted by organic matter, as indicated by their high concentrations of chemical oxygen demand (COD) and total organic carbon (TOC). Among the 11 heavy metals analyzed, Zn was the most enriched in all samples, followed by Mn, Fe, and Cu, whereas the concentrations of Pb, Cr, Cd, As, Ni, Sb, and Co were comparatively low. The results suggested that typical traffic emissions, natural events, industrial practices, and human activities were mainly sources of heavy metals. Low molecular-weight (LMW) PAHs were the dominant sources in snowmelt and snowpack. Anthracene (Ant) and fluorene (Flo) were the most enriched PAHs in both snowmelt surface runoff and snowpack. Coal burning for heating and traffic activities were the most important contributors of PAH pollutants in snowmelt surface runoff and snowpack in Beijing in the winter. Ecological risk assessment demonstrated, however, that heavy metals in snowmelt surface runoff pose little risk to downstream aquatic environments. A middle potential ecological risk could be caused by Ant, Flo, benzo[g, h, i]perylene (BghiP), and benzo[a]pyrene (BaA).

Groundwater quality assessment of the Takelsa phreatic aquifer (Northeastern Tunisia) using geochemical and statistical methods: implications for aquifer management and end-users

The Takelsa phreatic aquifer (Northeastern Tunisia) is an important source of fresh water for different economic sectors in the region that are strongly dependent on groundwater resources but, the aquifer is showing increasing signs of groundwater quality degradation like many other regions in the Mediterranean Basin. By integrating geochemical and multivariate statistical investigation methods, this research aims to identify the main geochemical processes and anthropogenic activities that are responsible for regional groundwater quality evolution, identifying the origins of salinity and nutrients, and their implications for groundwater use forcropirrigation and drinking water supply in order to improve aquifer management practices. The results show that groundwater facies vary from Ca-Mg-SO₄ to Na-Cl water type and that mineralization is strongly controlled by mineral dissolution and cation exchange. The isotopic analyses indicate that groundwater is recharged by rainwater infiltration at higher altitudes and that a cumulative evaporative effect may contribute to local increase of salt content in groundwater. The Water Quality Index (WQI) used to determine the suitability of the Takelsa groundwater for drinking purposes reveals that just half of the groundwater points sampled show good to excellent quality for human consumption. The groundwater quality is also limited for irrigation purposes due to anthropogenic activities existing throughout the region. As groundwater in the studied region is crucial for irrigation and human supply, the identified groundwater quality problems and the identification of the main processes responsible for them should contribute to improve the infrastructure and managementpractices to allow the region to sustainable exploit the available groundwater resources.

Latent class analysis of the association between polycyclic aromatic hydrocarbon exposures and body mass index

BACKGROUND

People experience multiple co-occurring exposures to environmental pollutants, but analyses of multiple exposures have rarely been reported.

OBJECTIVES

We used latent class analysis to estimate co-exposures to multiple polycyclic aromatic hydrocarbons (PAH), and tested the associations of latent classes to body mass index.

METHODS

We analyzed National Health and Nutrition Examination Survey (NHANES) 2013-2014 data. The sample included 2354 people aged 6-80 years. Measures included seven urinary PAH metabolites, BMI, and demographic and behavioral covariates. People were classified into mutually exclusive latent classes characterized by unique profiles of multiple PAH exposures. These classes were used as categorical independent variables in weighted multiple regression models with BMI as the dependent measure. Models were analyzed overall and by age groups (6-19, 20-59, and 60 and over.) We compared results using latent classes to results using a summed PAH exposure measure.

RESULTS

Five latent classes were identified. Two of these classes were significantly associated with higher BMI overall (p < .0001) and for the two youngest age groups. One of these classes was characterized by high multiple exposures across all PAHs, and one by moderate exposures but relatively high naphthalene and phenanthrene. The summed PAH score was associated with higher BMI only for the youngest age group.

CONCLUSIONS

Persons experience multiple co-exposures to PAHs that are related to BMI and obesity across age groups. Latent class analysis provides information on higher order interactions among multiple chemicals that a summed score does not. Future work may apply this approach to other outcomes or types of co-exposures.

Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products

Air quality monitoring across Europe is mainly based on in situ ground stations, which are too sparse to accurately assess the exposure effects of air pollution for the entire continent. The demand for precise predictive models that estimate gridded geophysical parameters of ambient air at high spatial resolution has rapidly grown. Here, we investigate the potential of satellite-derived products to improve particulate matter (PM) estimates. Bayesian geostatistical models addressing confounding between the spatial distribution of pollutants and remotely sensed predictors were developed to estimate yearly averages of both, fine (PM2.5) and coarse (PM10) surface PM concentrations, at 1 km2 spatial resolution over 46 European countries. Model outcomes were compared to geostatistical, geographically weighted and land-use regression formulations. Rigorous model selection identified the Earth observation data which contribute most to pollutants' estimation. Geostatistical models outperformed the predictive ability of the frequently employed land-use regression. The resulting estimates of PM10 and PM2.5, which represent the main air quality indicators for the urban Sustainable Development Goal, indicate that in 2016, 66.2% of the European population was breathing air above the WHO air quality guidelines thresholds. Our estimates are readily available to policy makers and scientists assessing the effects of long-term exposure to pollution on human and ecosystem health.

Studies on the Spatiotemporal Variability of River Water Quality and Its Relationships with Soil and Precipitation: A Case Study of the Mun River Basin in Thailand

Human activities can affect soil nutrients, thereby influencing river water quality. The spatial pattern of precipitation also impacts distributions of water quality. In this paper, we employed a method that combines point survey, soil, and water quality data to analyze the spatial relationships between precipitation, soil nutrient and water quality in the basin on the basis of field surveys and laboratory analysis. The ordinary kriging method was applied to interpolate the precipitation and soil data, and the spatial pattern was analyzed. The water samples on the main stream and soil samples in the field were collected during both the dry and rainy seasons to analyze the water quality and soil nutrients. The results indicate: (1) The water quality in the dry season is better than that in the rainy season, the water quality in the upper reaches is better than that in the lower reaches, and agricultural activity is the direct source of water pollution. (2) The precipitation in the rainy and dry seasons is differente and the dilution effect of precipitation on pollutant concentrations and transport of water flow affect the spatial distribution of water quality. (3) There is a significant difference in the spatial pattern of soil nutrients between the dry and rainy seasons, and the soil nutrient content and the surface runoff directly affect the water quality. Soil nutrients are affected by human activities, and they potentially act as nonpoint source (NPS) pollution in this river basin. To improve the water quality, suitable agriculture measures need to be implemented.

Abundance and distribution of beach litter along the Atlantico Department, Caribbean coast of Colombia

A total of 5993 litter items divided into 13 categories were found at 25 beaches located along the Atlantico Department coastline, Caribbean of Colombia, with an average litter abundance of 7 items/m. Agglomerative Hierarchical Clustering (AHC), Multidimensional Scaling (MDS) and Principal Components Analysis (PCA) were applied with the objective of highlighting similarities and contrasts between litter categories and abundances. Results indicated two specific groups of beaches in terms of amounts of litter. The first group is composed of 17 "dirty beaches" (urban, resort and village) while the second group includes 8 "clean beaches" (village and resort). This division was confirmed by means of the EA/NALG (2000) grading system, which highlighted that 68% of beaches belonging to the Atlantico Department coastline are in an unacceptable condition of cleanness. Current patterns of litter abundance and accumulation are related to sources as well as beach characteristics such as degree of exposition and morphodynamic state.

An advanced three-way factor analysis model (SDABB model) for size-resolved PM source apportionment constrained by size distribution of chemical species in source profiles

Source samples including crustal dust, cement dust, coal combustion were sampled and ambient samples of PM_2.5 and PM₁₀ were synchronously collected in Hefei from April to December 2014. The size distributions of the markers in the measured source profiles were incorporated into ME-2 solution to develop a new method, called the SDABB model (an advanced ABB three-way factor analysis model incorporating size distribution information). The performance of this model was investigated using three-way synthetic and ambient dataset. For the synthetic tests, the size distributions of markers estimated by the SDABB model were more consistent with true condition. The AAEs between estimated and observed contributions of the SDABB ranged from 15.2% to 29.0% for PM₁₀ and 19.9%-31.6% for PM_2.5, which is lower than those of PMF2. For the ambient PM, six source categories were identified by SDABB for both sizes, although the profiles were different. The source contributions were sulphate (33.33% and 24.53%), nitrate and SOC (22.33% and 18.16%), coal combustion (19.01% and 18.23%), vehicular exhaust (12.99% and 12.07%), crustal dust (10.69% and 19.40%) and cement dust (1.65% and 5.39%) for PM_2.5 and PM₁₀ respectively. In addition, the estimated ratios of Al, Si, Ti and Fe in CRD were 0.76, 0.84, 1.10 and 0.85; those of Al and Si in CC were 0.42 and 0.66; Ca and Si in CD were 0.95 and 1.10; NO₃^- and NH₄⁺ in nitrate were 1.11 and 1.01; and SO₄^2- and NH₄⁺ in sulphate were 0.96 and 1.16. These modeled ratios were consistent with the measured ratios. The size distribution of contributions also came close to reality. Thus, the advanced SDABB three-way model can better capture the characteristics of sources between sizes by effectively incorporating the size distributions of the markers as physical constraints.

VegeSafe: a community science program generating a national residential garden soil metal(loid) database

VegeSafe is a national community science initiative aimed at characterising soils in Australian residential gardens and community gardens. The program has been operating for over 5 years and has generated soil metal(loid) data from over 8600 residential garden and community garden soil samples, submitted by almost 2000 community scientists. The VegeSafe program represents the largest archive of soil metal(loid) data and associated metadata for residential garden soils in Australia. Samples were collected across Australia, with 61% of samples collected from NSW (n = 5284), Victoria (VIC) 20% (n = 1688) of samples and Queensland (QLD) 7% (n = 592) of samples. Soil metal(loid) data obtained by analysis of bulk soil samples by portable X-ray florescence spectrometry (pXRF) for As, Cu, Cr Mn, Pb and Zn showed spatial patterns of greater soil metal(loid) concentrations around city areas, particularly in NSW and VIC. The Australian Health Investigation Levels for low-density residential land uses (HIL-A) were used in this study as guideline values for soil metal(loid) concentrations. Overall, there was a relatively small number of HIL-A exceedances in the dataset, with most metal(loid)s exceeding their HIL-A concentration in < 5% of incidences. The notable exception to this was for Pb, which had HIL-A (300 mg/kg) exceeded in 27% (n = 1427) of samples in NSW, 17% (n = 280) in VIC and 10% (n = 61) in QLD. Through the power of community engagement and community science, the VegeSafe program presents an unprecedented insight into soil metal(loid) concentrations in Australian residential gardens.