Active environmental surveillance of SARS-CoV-2 in Midwestern United States meatpacking plants

This pilot project investigated environmental SARS-CoV-2 presence in seven Midwestern meatpacking plants from May 2020 to January 2021. This study investigated social distancing and infection control practices and incorporated environmental sampling of surfaces and air in employee common areas. All plants increased their social distancing efforts, increased the frequency of cleaning and disinfecting worker areas, and screened for symptomatic people to prevent entry into the workplace. 575 samples from common areas were collected and evaluated with RT-qPCR for the presence of SARS-CoV-2. 42/367 surface samples were positive, while no virus was detected in air samples. Case positive data from the counties surrounding each plant showed peak positive SARS-CoV-2 cases from 12-55 days before the virus was detected in the plant, indicating that environmental sampling is likely a lagging indicator of community and plant infection.

Danube as a symbol of Europe. Perception of the river from varied geographical perspectives

The Danube is promoted as a pan-European river, what can be justified for instance by the vast range of its drainage basin, covering 19 countries on both sides of the historical border diving Eastern and Western Europe. Differentiation of imaginations of Danube course from the perspective of 7 European cities, based on research covering 1577 respondents, conducted between 2005–2007 and 2016–2018 has been presented in the paper. Maps presenting the generalized imagination of river course have been generated for each city. It has been proved that in spite of substantial political, economical and symbolical importance of this river for big part of Europe, the course of Danube remains unknown for inhabitants of its Western part, in parallel to more correct recognition of the river by students from Eastern Europe. It has been shown that the perception does not change despite the progressing integration processes.

HEALTH IN THE SYSTEM OF VALUES OF THE POPULATION OF THE SURVEILLANCE ZONES NUCLEAR POWER PLANTSS

OBJECTIVE

to determine the place of health in the system of values of the population of the surveillance zone (SZ)of nuclear power plants (NPPs) and its importance in the perception of emergency risks (ER).

MATERIALS AND METHODS

To determine the place of health in the value system, a survey of the able-bodied popula-tion of satellite cities of Rivne (RNPP) and South Ukrainian (SUNPP) nuclear power plants was conducted using non-repetitive sampling, where the sampling error does not exceed 7,0 %. The motivational and behavioral componentthat determined health in the individual hierarchy of values of the subject according to the questionnaireBerezovskaya R. A. was studied. Statistical and mathematical methods were used in the research process.

RESULTS

The array of respondents was conditionally divided into 4 groups according to their attitude to humanhealth. And the group where a person's life position is focused exclusively on health is the most common - 77,0 %.Group IV, which wants to live without limiting itself, is 8,1 %. The component integrity of values-goals and values-means among the urban population of the SZ of both nuclear power plants is the same: the main goal in life is health,happy family life, and as a means - perseverance, diligence and health. Goal values in groups I and IV have somedifferences: in the first group of respondents the main goal in life is health, and in the fourth, where a person's lifeguidelines exclude any restrictions - a happy family life. Values for these populations have some differences, but inboth groups health appears to be the main means to an end. There is a close correlation between the core of termi-nal values and the average indicators of the state of concern about the risk of emergencies.

CONCLUSIONS

Identified hierarchy of values: a group of stable dominant values; average status values; group of leastsignificant values. The values of the highest status among the values-goals are - health, happy family life and inte-resting work. Most respondents plan to achieve them through values such as «health», «perseverance and hardwork». There is a close correlation between the core of terminal values and the average indicators of the state ofconcern about the risk of emergencies.

Positive effects of COVID-19 lockdown on river water quality: evidence from River Damodar, India

The global economic activities were completely stopped during COVID-19 lockdown and continuous lockdown partially brought some positive effects for the health of the total environment. The multiple industries, cities, towns and rural people are completely depending on large tropical river Damodar (India) but in the last few decades the quality of the river water is being significantly deteriorated. The present study attempts to investigate the river water quality (RWQ) particularly for pre- lockdown, lockdown and unlock period. We considered 20 variables per sample of RWQ data and it was analyzed using novel Modified Water Quality Index (MWQI), Trophic State Index (TSI), Heavy Metal Index (HMI) and Potential Ecological Risk Index (RI). Principal component analysis (PCA) and Pearson's correlation (r) analysis are applied to determine the influencing variables and relationship among the river pollutants. The results show that during lockdown 54.54% samples were brought significantly positive changes applying MWQI. During lockdown, HMI ranged from 33.96 to 117.33 with 27.27% good water quality which shows the low ecological risk of aquatic ecosystem due to low mixing of toxic metals in the river water. Lockdown effects brought river water to oligotrophic/meso-eutrophic condition from eutrophic/hyper-eutrophic stage. Rejuvenation of river health during lockdown offers ample scope to policymakers, administrators and environmentalists for restoration of river health from huge anthropogenic stress.

Appraisal of metallic pollution and ecological risks in agricultural soils of Alborz province, Iran, employing contamination indices and multivariate statistical analyses

The current study was done to evaluate the pH, electrical conductivity, bulk density, sand, silt, clay, available K and P, organic carbon, Zn, Cu, Mn, and Fe in agricultural soils of Alborz province, Iran. A total of 46 samples were collected as composite samples from 0-20 and 20-40 cm soil depths. The average values of Fe, Cu, Mn, and Zn were found lower than the European limits, Indian limits, and Earth's crust. Pearson's correlation analysis found that pH effects Zn retention and, Cu and Mn retention in soil samples. Cluster analysis and Principal component analysis established that HMs are originated from different sources. Contamination factor (CF) and ecological risk index (RI) results showed less contamination and ecological risk in soil samples. Enrichment factor (EF) and modified ecological risk index (MRI) results indicated high enrichment and ecological risk of Cu, Mn, and Zn in surface and sub-surface soil samples.

Indoor and outdoor noise changes due to the COVID-19 lockdown and their effects on individuals' expectations and preferences

The COVID-19 pandemic significantly modified our urban territories. One of the most strongly affected parameters was outdoor noise, caused by traffic and human activity in general, all of which were forced to stop during the spring of 2020. This caused an indubitable noise reduction both inside and outside the home. This study investigates how people reacted to this new unexpected, unwanted and unpredictable situation. Using field measurements, it was possible to demonstrate how the outdoor sound pressure level clearly decreased. Furthermore, by means of an international survey, it was discovered that people had positive reaction to the lower noise level. This preference was generally not related to home typology or location in the city, but rather to a generalized wish to live in a quieter urban environment.

Quantifying nitrate pollution sources of the drinking water source area using a Bayesian isotope mixing model in the northeastern suburbs of Beijing, China

Nitrate pollution has become an environmental problem of global concern. One effective way for controlling the nitrate pollution of water is to identify the pollution source and reduce the input of nitrate. This study traces and quantifies the sources of nitrate contamination to groundwater and surface water in the northeastern suburbs of Beijing, where an emergency groundwater source zone is located. Nitrogen and oxygen stable isotope analysis, geospatial analysis techniques, principal component analysis, correlation analysis, and a Bayesian isotope mixing model were used to achieve our goals. The results show that the main sources of nitrate pollution in groundwater were manure and sewage (M&S) (42.6 %) > soil nitrogen (SN) (26.6 %) > NH4+ in fertilizer and rain (NHF&R) (24.5 %) > NO3- fertilizer (NOF) (5.0 %) > NO3- in atmospheric deposition (NAD) (1.3 %), and main sources of nitrate in surface water were M&S (28.8 %) > SN (20.4 %) > NAD (19.8%) > NOF (16.5%) > NHF&R (14.5 %). Due to the high permeability of the aquifer in the study area, there was a strong hydraulic connection between groundwater and surface water. The discharge of treated wastewater (reclaimed water) into the mostly dried river channel in the study area might aggravate nitrate pollution in the groundwater.

Quantifying responses to changes in the jurisdiction of a congestion charge: A study of the London western extension

This paper quantifies behavioural responses to changes in the jurisdiction of a congestion charge, with a successive focus on (i) an extension and (ii) a reduction in the size of the charging zone. We exploit the unanticipated nature of both the implementation and removal of London's Western Expansion Zone (WEZ) as quasi-natural experiments to test whether individual responses to policies are asymmetric. We use the UK Department of Transport Annual Average Daily Flow (AADF) data, which records traffic flows for seven transport modes (including cars, buses, bicycles, heavy and light goods vehicles). Using a difference-in-differences approach, we find that the introduction of the WEZ led to a 4.9% decline in road traffic flows in the new congestion charge area. These results are robust to different model specifications. HGVs traffic did not significantly change post-WEZ, which indicates that their road demand is price inelastic. The removal of the WEZ led to no significant variations in traffic. This result indicates asymmetry in behaviour with persistent changes in post-intervention traffic demand levels.

Air quality-related health damages of food

Agriculture is a major contributor to air pollution, the largest environmental risk factor for mortality in the United States and worldwide. It is largely unknown, however, how individual foods or entire diets affect human health via poor air quality. We show how food production negatively impacts human health by increasing atmospheric fine particulate matter (PM2.5), and we identify ways to reduce these negative impacts of agriculture. We quantify the air quality-related health damages attributable to 95 agricultural commodities and 67 final food products, which encompass >99% of agricultural production in the United States. Agricultural production in the United States results in 17,900 annual air quality-related deaths, 15,900 of which are from food production. Of those, 80% are attributable to animal-based foods, both directly from animal production and indirectly from growing animal feed. On-farm interventions can reduce PM2.5-related mortality by 50%, including improved livestock waste management and fertilizer application practices that reduce emissions of ammonia, a secondary PM2.5 precursor, and improved crop and animal production practices that reduce primary PM2.5 emissions from tillage, field burning, livestock dust, and machinery. Dietary shifts toward more plant-based foods that maintain protein intake and other nutritional needs could reduce agricultural air quality-related mortality by 68 to 83%. In sum, improved livestock and fertilization practices, and dietary shifts could greatly decrease the health impacts of agriculture caused by its contribution to reduced air quality.

Environmental Surveillance for Polioviruses in Haïti (2017-2019): The Dynamic Process for the Establishment and Monitoring of Sampling Sites

Haïti is at risk for wild poliovirus (WPV) importation and circulation, as well as vaccine-derived poliovirus (VDPV) emergence. Environmental surveillance (ES) for polioviruses was established in Port au Prince and Gonaïves in 2016. During 2017–2019, initial ES sites were re-evaluated, and ES was expanded into Cap Haïtien and Saint Marc. Wastewater samples and data on weather, hour of collection, and sample temperature and pH were collected every 4 weeks during March 2017–December 2019 (272 sampling events) from 21 sites in Cap Haïtien, Gonaïves, Port au Prince, and Saint Marc. Samples were processed for the detection of polio and non-polio enteroviruses using the two-phase and “Concentration and Filter Elution” methodologies. Polioviruses were serotyped and underwent intra-typic characterization. No WPV or VDPVs were isolated. Sabin-like polioviruses (oral vaccine strain) of serotypes 1 and 3 were sporadically detected. Five of six (83%), one of six (17%), five of six (83%), and two of three (67%) sites evaluated in Cap Haïtien, Gonaïves, Port au Prince, and Saint Marc, respectively, had enterovirus isolation from >50% of sampling events; these results and considerations, such as watershed population size and overlap, influence of sea water, and excessive particulates in samples, were factors in site retention or termination. The evaluation of 21 ES sampling sites in four Haïtian cities led to the termination of 11 sites. Every-four-weekly sampling continues at the remaining 10 sites across the four cities as a core Global Polio Eradication Initiative activity.

Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India

On January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM10, PM2.5, CO, NO2, O3, and SO2). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM10, PM2.5, CO, SO2), and an increment in ozone concentration was observed due to a drastic reduction in NO2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.

Assessment of groundwater safety surrounding contaminated water storage sites using multivariate statistical analysis and Heckman selection model: a case study of Kazakhstan

Petrochemical enterprises in Kazakhstan discharge polluted wastewater into special recipients. Contaminants infiltrate through the soil into the groundwater, which potentially affects public health and environment safety. This paper presents the evaluation of a 7-year monitoring program from one of the factories and includes nineteen variables from nine wells during 2013-2019. Several multivariate statistical techniques were used to analyse the data: Pearson's correlation matrix, principal component analysis and cluster analysis. The analysis made it possible to specify the contribution of each contaminant to the overall pollution and to identify the most polluted sites. The results also show that concentrations of pollutants in groundwater exceeded both the World Health Organization and Kazakhstani standards for drinking water. For example, average exceedance for total petroleum hydrocarbons was 4 times, for total dissolved solids-5 times, for chlorides-9 times, for sodium-6 times, and total hardness was more than 6 times. It is concluded that host geology and effluents from the petrochemical industrial cluster influence the groundwater quality. Heckman two-step regression analysis was applied to assess the bias of completed analysis for each pollutant, especially to determine a contribution of toxic pollutants into total contamination. The study confirms a high loading of anthropogenic contamination to groundwater from the petrochemical industry coupled with natural geochemical processes.

Marine Heatwaves

Ocean temperature variability is a fundamental component of the Earth's climate system, and extremes in this variability affect the health of marine ecosystems around the world. The study of marine heatwaves has emerged as a rapidly growing field of research, given notable extreme warm-water events that have occurred against a background trend of global ocean warming. This review summarizes the latest physical and statistical understanding of marine heatwaves based on how they are identified, defined, characterized, and monitored through remotely sensed and in situ data sets. We describe the physical mechanisms that cause marine heatwaves, along with their global distribution, variability, and trends. Finally, we discuss current issues in this developing research area, including considerations related to thechoice of climatological baseline periods in defining extremes and how to communicate findings in the context of societal needs.

Geochemical and Multivariate Statistical Evaluation of Trace Elements in Groundwater of Niğde Municipality, South-Central Turkey: Implications for Arsenic Contamination and Human Health Risks Assessment

This study was conducted to determine the concentrations of trace elements, their sources, and human health risks associated with arsenic contamination in groundwater of the Niğde Municipality, south-central Turkey. Fourteen groundwater samples were collected from groundwater supply sources fed by the Niğde water distribution system and were analysed for Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, and Ba concentrations. Multivariate statistical analyses were applied to decipher the source and interrelationships among trace elements in groundwater. The groundwater is mainly tapped from Quaternary alluvial and volcanic aquifers of the Niğde Massif. The pH of groundwater is slightly acidic to neutral, which controls the solubility and mobility of the trace elements. The mean concentrations of the trace elements vary in the order Zn > Fe > Ba > As > Cr > Ni > Se > Cu > Co > Mn > Al. All of the trace element concentrations comply with the maximum permissible values provided by the Turkish Standards Institution and the World Health Organization, except Zn, Cr, and As. However, approximately 7.14% of the studied samples are contaminated with Zn and Cr, whereas 86% are contaminated with As. The As concentrations range from 9.47 to 32.9 µg/L with an average value of 16.8 µg/L. Contamination assessment indicates that the As contamination is dominant in the southern and southwestern parts of the area. The primary source of As in groundwater is attributed to geogenic processes involving weathering and dissolution of bed rocks and other factors, such as pH conditions, adsorption, and surface complexation. Three bimetallic complex associations are distinguished in groundwater: Fe-coordination group, As-coordination group and Ba-coordination group, all showing strong positive correlation with Cu and Ni. The As-coordination group is the most dominant in groundwater, which resulted in the high As content of groundwater. Multivariate statistical analyses indicate that As mobilization in groundwater is associated with pH, EC, Ni, Cu, and Ba depending on the redox conditions of the aquifer, controlled mainly by geogenic processes. The carcinogenic risk of arsenic affecting children and adults reaches 2 × 10^-4 and 3 × 10^-4, respectively, exceeding the guideline value of 1 × 10^-4. The estimated hazard quotient for children is in the range of 1.79-6.21, whereas that of adults is 0.77-2.66, indicating that children in the municipality are more exposed to the noncarcinogenic effects of the consumption of high groundwater arsenic.

Marine Heatwaves

Ocean temperature variability is a fundamental component of the Earth's climate system, and extremes in this variability affect the health of marine ecosystems around the world. The study of marine heatwaves has emerged as a rapidly growing field of research, given notable extreme warm-water events that have occurred against a background trend of global ocean warming. This review summarizes the latest physical and statistical understanding of marine heatwaves based on how they are identified, defined, characterized, and monitored through remotely sensed and in situ data sets. We describe the physical mechanisms that cause marine heatwaves, along with their global distribution, variability, and trends. Finally, we discuss current issues in this developing research area, including considerations related to thechoice of climatological baseline periods in defining extremes and how to communicate findings in the context of societal needs.

Polycyclic aromatic hydrocarbons (PAHs) cycling and fates in Galveston Bay, Texas, USA

The cycling and fate of polycyclic aromatic hydrocarbons (PAHs) is not well understood in estuarine systems. It is critical now more than ever given the increased ecosystem pressures on these critical coastal habitats. A budget of PAHs and cycling has been created for Galveston Bay (Texas) in the northwestern Gulf of Mexico, an estuary surrounded by 30-50% of the US capacity of oil refineries and chemical industry. We estimate that approximately 3 to 4 mt per year of pyrogenic PAHs are introduced to Galveston Bay via gaseous exchange from the atmosphere (ca. 2 mt/year) in addition to numerous spills of petrogenic PAHs from oil and gas operations (ca. 1.0 to 1.9 mt/year). PAHs are cycled through and stored in the biota, and ca. 20 to 30% of the total (0.8 to 1.5 mt per year) are estimated to be buried in the sediments. Oysters concentrate PAHs to levels above their surroundings (water and sediments) and contain substantially greater concentrations than other fish catch (shrimp, blue crabs and fin fish). Smaller organisms (infaunal invertebrates, phytoplankton and zooplankton) might also retain a significant fraction of the total, but direct evidence for this is lacking. The amount of PAHs delivered to humans in seafood, based on reported landings, is trivially small compared to the total inputs, sediment accumulation and other possible fates (metabolic remineralization, export in tides, etc.), which remain poorly known. The generally higher concentrations in biota from Galveston Bay compared to other coastal habitats can be attributed to both intermittent spills of gas and oil and the bay's close proximity to high production of pyrogenic PAHs within the urban industrial complex of the city of Houston as well as periodic flood events that transport PAHs from land surfaces to the Bay.

Establishing a Community Air Monitoring Network in a Wildfire Smoke-Prone Rural Community: The Motivations, Experiences, Challenges, and Ideas of Clean Air Methow's Clean Air Ambassadors

In response to wildfire-related air quality issues as well as those associated with winter wood stove use and prescribed and agricultural burning, Clean Air Methow's Clean Air Ambassador program established a community air monitoring network (CAMN) to provide geospatially specific air quality information and supplement data generated by the two Washington State Department of Ecology nephelometers situated in the area. Clean Air Ambassadors (CAAs) were purposefully selected to host low-cost air sensors based on their geographic location and interest in air quality. All 18 CAAs were interviewed to understand their motivations for participation, experiences using the data, challenges encountered, and recommendations for future project directions. Interview transcripts were coded, and a qualitative analysis approach was used to identify the key themes in each domain. The reported motivations for participation as a CAA included reducing personal exposure, protecting sensitive populations, interest in air quality or environmental science, and providing community benefits. CAAs used CAMN data to understand air quality conditions, minimize personal or familial exposure, and engage other community members in air quality discussions. Opportunities for future project directions included use for monitoring other seasonal air quality issues, informing or reducing other pollution-generating activities, school and community educational activities, opportunities for use by and engagement of different stakeholder groups, and mobile-friendly access to CAMN information. Limited challenges associated with participation were reported. Additional research is necessary to understand the community-level impacts of the CAMN. The findings may be informative for other rural wildfire smoke-prone communities establishing similar CAMNs.

Ecological status of a freshwater tectonic lake of the indo-burmese province: Implications for livelihood development

Tectonic lakes are among the most geologically fascinating and environmentally versatile hydrobiological systems found on the earth's surface. We conducted a study on the limnology of Tasek Lake, a tectonic lake located in the Indo-Burma Province of the South Asian region. Physico-chemical parameters of the lake's water along with its plankton were considered for the study. Their relationship was analysed by understanding their seasonal variations and through linear regression models. The water quality index (WQI), plankton diversity indices and canonical correspondence analysis (CCA) were computed. The ichthyofaunal diversity was also studied to get an insight into the lake's fishery potential. A preliminary assessment on the economic feasibility of converting Tasek Lake into a fishery was also completed. Results indicate moderate eutrophication in the lake and the plankton population is observed to be rich and abundant. The WQI value confirms the water to be of "very poor" quality. The CCA was done to analyze the relationships of physico-chemical parameters with months and seasons, and the relation between seasons and plankton assemblages. Results corroborate the results of WQI. Identified fish population suggest ample fishery potential of the lake. The economic assessment reveals that in order to maintain the ecological sustainability of the lake, it should be transformed into a recreational fishery, following a catch-and-release model. The study calls for urgent restoration of the lake so that not only its pristine ecology is survived but also its fishery potential is sustainably harnessed and local livelihood is improved.

Monte Carlo bottom-up evaluation of global instrumental quantification uncertainty: Flexible and user-friendly computational tool

Many instrumental methods of analysis require the daily collection of calibrator signals to calibrate their response. The quality of quantifications based on these calibrations depends on calibrators quality, instrumental signal performance and regression model fitness. Linear Ordinary Least Squares (LOLS), Linear Weighted Least Squares (LWLS) or Linear Bivariate Least Squares (LBLS) regression models can be used to calibrate and evaluate the uncertainty from instrumental quantifications, but require the fulfilment of some assumptions, namely, constant signal variance (LOLS), high calibrators quality (LOLS and LWLS) and linear variation of instrumental signal with calibrator values. The LBLS is flexible regarding calibrator values uncertainty and correlation but requires the determination of calibrator values and signals covariances. This work developed a computational tool for the bottom-up evaluation of global instrumental quantifications uncertainty which simulates calibrator values correlations from entered calibrators preparation procedure and simulates calibrators and samples signals precision from prior precision data, allowing accurate uncertainty evaluation from a few replicate signals of the daily calibration. The used signal precision models were built from previously observed repeatability variation throughout the calibration interval adjusted to daily precision condition from a residual standard deviation adjustment factor. This approach was implemented in a user-friendly MS-Excel file and was successfully applied to the analysis of As, Cd, Ni and Pb in marine sediment extracts by Absorption Spectroscopy. Evaluations were tested by the metrological compatibility of estimated and reference values of control standards for confidence levels of 95% and 99%. The success rates of the compatibility tests were statistically equivalent to the confidence level (p-value>0.01).

Source apportionment of water pollutants in the upstream of Yangtze River using APCS-MLR

As the upper reach of the Yangtze River, the Jinsha River has experienced ecological degradation due to increased anthropogenic activities. The potential pollution sources affecting the Jinsha River watershed from 2016 to 2018 were investigated using an improved method in combination with correlation analysis and the absolute principal component score-multiple linear regression receptor modeling technique. Our results identified 5-7 potential pollution sources in the Jinsha main stream watershed and the Pudu, Niulan, and Yalong River watersheds of the Jinsha River. The water pollutant concentrations of the Jinsha main stream watershed were mainly influenced by environmental, agricultural, and human population factors. In the Pudu River watershed, the primary pollution sources changed to natural and sedimentary pollutant sources. It is necessary to control the sedimentary pollutants. The Niulan River watershed was also influenced by natural environment factors. Among those, mineral, sedimentary pollutant, and meteorological sources contributed the most to water quality. In the case of the Yalong River watershed, the influence of non-point source pollution caused by human activities and sedimentary pollutants was the main reason for the deterioration of the ecological environment. The multivariate statistical techniques presented good adaptability for the analysis of pollution sources in the Jinsha River watershed, and the results may be useful for the protection and management of the watershed eco-environment.

Coastal pollution from the industrial park Quintero bay of central Chile: Effects on abundance, morphology, and development of the kelp Lessonia spicata (Phaeophyceae)

The industrial park of Quintero Bay (QB) in the central coast of Chile was established in the 1960s, presents high levels of pollution due to the industrial activity, and it is known as one of the five Chilean "sacrifice zones". Lessonia spicata is the most important habitat-forming kelp species in the intertidal along the central and south shores of Chile, and currently there are no morphometric and population studies of L. spicata (or other seaweed species) nor studies about the effects of pollution on its development in QB and neighbouring sites. In this context, the aims of this study were (i) to register the abundance and morphological features of L. spicata populations from Ventanas, Horcón and Cachagua (sites with different pollution histories and located only up to 40 km from the QB); ii) to determine the heavy metals (HMs) concentration in seawater and marine sediments; and (iii) to evaluate in vitro the effects of exposure to seawater from the three sampling sites on spore release and early developmental stages, up to the juvenile sporophyte. Results showed that the chronically exposed Ventanas kelp population had the smallest adult individuals in comparison with the other sites. Ventanas and Horcón registered high HMs concentration in the seawater and marine sediments exceeding the international permissible limits (e.g in seawater Cu 20-859 μg L-1; sediments Cu > 50,000 μg kg-1). Unexpectedly in Cachagua, a site often considered unpolluted, high concentrations of Cu and As were also registered in the seawater (859 and 1,484 μg L-1, respectively) and of As in marine sediments (20,895 μg kg-1). Exposure of gametophytes to the seawater from Ventanas resulted in a developmental delay compared to the other treatments; however, low sporophyte production was determined in all treatments. Our results indicate that QB, more notably Ventanas, induce highly negative effects on individual development, and consequently on seaweed populations, which suggest a long-term negative impact on the community structure of these marine zones. Furthermore, the high concentrations of HMs reported here at Cachagua suggest a recent expansion of pollution along the central coast of Chile, evidencing effects on the marine ecosystem health even on sites far from the pollution source.

SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.

Association of Fine Particulate Matter and Risk of Stroke in Patients With Atrial Fibrillation

IMPORTANCE

Air pollution is associated with cardiovascular outcomes. Specifically, fine particulate matter measuring 2.5 μm or less (PM2.5) is associated with thrombosis, stroke, and myocardial infarction. Few studies have examined particulate matter and stroke risk in individuals with atrial fibrillation (AF).

OBJECTIVE

To assess the association of residential-level pollution exposure in 1 year and ischemic stroke in individuals with AF.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 31 414 individuals with AF from a large regional health care system in an area with historically high industrial pollution. All participants had valid residential addresses for geocoding and ascertainment of neighborhood-level income and educational level. Participants were studied from January 1, 2007, through September 30, 2015, with prospective follow-up through December 1, 2017. Data analysis was performed from March 14, 2018, to October 9, 2019.

EXPOSURES

Exposure to PM2.5 ascertained using geocoding of addresses and fine-scale air pollution exposure surfaces derived from a spatial saturation monitoring campaign and land-use regression modeling. Exposure to PM2.5 was estimated annually across the study period at the residence level.

MAIN OUTCOMES AND MEASURES

Multivariable-adjusted stroke risk by quartile of residence-level and annual PM2.5 exposure.

RESULTS

The cohort included 31 414 individuals (15 813 [50.3%] female; mean [SD] age, 74.4 [13.5] years), with a median follow-up of 3.5 years (interquartile range, 1.6-5.8 years). The mean (SD) annual PM2.5 exposure was 10.6 (0.7) μg/m3. A 1-SD increase in PM2.5 was associated with a greater risk of stroke after both adjustment for demographic and clinical variables (hazard ratio [HR], 1.08; 95% CI, 1.03-1.14) and multivariable adjustment that included neighborhood-level income and educational level (HR, 1.07; 95% CI, 1.00-1.14). The highest quartile of PM2.5 exposure had an increased risk of stroke relative to the first quartile (HR, 1.36; 95% CI, 1.18-1.58). After adjustment for clinical covariates, income, and educational level, risk of stroke remained greater for the highest quartile of exposure relative to the first quartile (HR, 1.21; 95% CI, 1.01-1.45).

CONCLUSIONS AND RELEVANCE

This large cohort study of individuals with AF identified associations between PM2.5 and risk of ischemic stroke. The results suggest an association between fine particulate air pollution and cardiovascular disease and outcomes.

Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt

Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas.

Bootstrap simulations to estimate relationships between Type I error, power, effect size, and appropriate sample numbers for bioassessments of aquatic ecosystems

The Extended Bootstrap (EB) assessment approach was developed for the examination of relationships of Type I error, power, sample size (n), and effect size (ES) for statistical tests of ecological data. The EB approach was applied to univariate and multivariate statistical analyses of a large data set collected from an ongoing, multiple stressor bioassessment study of watersheds in the Central Valley, San Francisco, and Central Coast areas of California. Benthic metrics were created that either increased or decreased monotonically with stress (toxicants or metrics indicative of habitat quality). Type I errors were stable for all statistical tests that were evaluated. The relationships between n and ES displayed patterns of "diminishing returns" for all statistical tests: i.e. an increasingly larger n was required to detect decreasingly smaller ES. Nonetheless, the n's collected across the watersheds and within a selected watershed were sufficient to detect even small correlations between representative benthic metrics and potential stressors with high power. The power and robustness of a novel method using EB and previously described statistical techniques designed to address multicollinearity were shown to approach those of simpler univariate regressions. Potential applications of the EB approach for experimental design, data assessment and interpretation, and hypothesis testing are discussed.

Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel—1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year’s flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year’s same season (August 2015–2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.

Exposure assessment of adults living near unconventional oil and natural gas development and reported health symptoms in southwest Pennsylvania, USA

Recent research has shown relationships between health outcomes and residence proximity to unconventional oil and natural gas development (UOGD). The challenge of connecting health outcomes to environmental stressors requires ongoing research with new methodological approaches. We investigated UOGD density and well emissions and their association with symptom reporting by residents of southwest Pennsylvania. A retrospective analysis was conducted on 104 unique, de-identified health assessments completed from 2012-2017 by residents living in proximity to UOGD. A novel approach to comparing estimates of exposure was taken. Generalized linear modeling was used to ascertain the relationship between symptom counts and estimated UOGD exposure, while Threshold Indicator Taxa Analysis (TITAN) was used to identify associations between individual symptoms and estimated UOGD exposure. We used three estimates of exposure: cumulative well density (CWD), inverse distance weighting (IDW) of wells, and annual emission concentrations (AEC) from wells within 5 km of respondents' homes. Taking well emissions reported to the Pennsylvania Department of Environmental Protection, an air dispersion and screening model was used to estimate an emissions concentration at residences. When controlling for age, sex, and smoker status, each exposure estimate predicted total number of reported symptoms (CWD, p<0.001; IDW, p<0.001; AEC, p<0.05). Akaike information criterion values revealed that CWD was the better predictor of adverse health symptoms in our sample. Two groups of symptoms (i.e., eyes, ears, nose, throat; neurological and muscular) constituted 50% of reported symptoms across exposures, suggesting these groupings of symptoms may be more likely reported by respondents when UOGD intensity increases. Our results do not confirm that UOGD was the direct cause of the reported symptoms but raise concern about the growing number of wells around residential areas. Our approach presents a novel method of quantifying exposures and relating them to reported health symptoms.

Assessing background particulate contamination in an historic building - surface lead loading and contamination

Investigation of suspect surface contamination in a building may require comparative sampling across different zones to provide meaningful information with regard to contaminant sources, pathways and/or extent of dispersal. However, evaluation of the data using traditional null hypothesis significance testing (NHST) based upon the mean may result in misleading inference when encountering erratic distributions typical of environmental contaminant data. Sampling data (n = 90) for lead content in surface dust collected throughout a historic building with suspect contamination from uncontrolled disturbance to lead coatings were evaluated using traditional NHST and randomization/permutation inference; the latter metric was the maximum difference in frequency of detection (Δfd _max), to directly calculate the probability of the observed differences. In the examples for lead in surface dust presented herein, areas with "lower" mean concentration and/or no significant difference via NHST actually represented "greater contamination," as Δfd _max indicated a greater probability of encountering lead at higher concentrations. Resulting conclusions with regard to sources and pathways contradicted those generated from traditional NHST, and underscore the need to recognize differences in applicability of different inference approaches, depending upon the distribution of the data and the particular problem. This is particularly relevant for forensic purposes.

IMPLICATIONS

The use of permutation/randomization inference to gain insight into sources and pathways of contamination may be more appropriate than the conventional Neyman/Pearson (N/P) logic in negative hypothesis significance testing (NHST). This suggests a broader understanding by environmental professionals of the assumptions and limitations of NHST and alternative inference such as through permutation/randomization is warranted.

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015-2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash-Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.

IMPLICATIONS

Guwahati is one of the most polluted cities in India provided a novel evidence to find out the impact of air pollution. Present study has been suffered from several limitations, like (i) the daily or weekly concentration of air pollutants was not gained due to limited monitoring technique, (2) dearth of regular information of PM2.5 collection, which were not regularly connected. Present study is used to estimate the spatio-temporal variations (2015-2017) of gaseous and particulate air pollutants using GIS and spatial statistical approach. Probably, this is the first study to report the spatial and temporal variation of air quality distribution in Assam. Results showed there is a negative impact on the ambient air quality status of Assam. These industries and mining areas contribute significantly to the air pollution in this deltaic region. This district-wise information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes. The dissimilarity in geographical dissemination of the pollutant concentration has been more helpful in seasonal inevitability. Consequently, a continuous set of data and more parameters can be included to attain more reliable results.

Long-term PM2.5 exposure increases the risk of non-small cell lung cancer (NSCLC) progression by enhancing interleukin-17a (IL-17a)-regulated proliferation and metastasis

PM_2.5 is a class of airborne particles and droplets with sustained high levels in many developing countries. Epidemiological studies have indicated that PM_2.5 is closely associated with the increased morbidity and mortality of lung cancer in the world. Unfortunately, the effects of PM_2.5 on lung cancer are largely unknown. In the present study, we attempted to explore the role of PM_2.5 in the etiology of NSCLC. Here, we found that long-term PM_2.5 exposure led to significant pulmonary injury. Epithelial-mesenchymal transition (EMT) and cancer stem cells (CSC) properties were highly induced by PM_2.5 exposure. EMT was evidenced by the significant up-regulation of MMP2, MMP9, TGF-β1, α-SMA, Fibronectin and Vimentin. Lung cancer progression was associated with the increased expression of Kras, c-Myc, breast cancer resistance protein BCRP (ABCG2), OCT4, SOX2 and Aldh1a1, but the decreased expression of p53 and PTEN. Importantly, mice with IL-17a knockout (IL-17a^-/-) showed significantly alleviated lung injury and CSC properties following PM_2.5 exposure. Also, IL-17a^-/--attenuated tumor growth was recovered in PM_2.5-exposed mice injected with recombinant mouse IL-17a, accompanied with significantly restored lung metastasis. Taken together, these data revealed that PM_2.5 could promote the progression of lung cancer by enhancing the proliferation and metastasis through IL-17a signaling.

The SaniPath Exposure Assessment Tool: A quantitative approach for assessing exposure to fecal contamination through multiple pathways in low resource urban settlements

Inadequate sanitation can lead to exposure to fecal contamination through multiple environmental pathways and can result in adverse health outcomes. By understanding the relative importance of multiple exposure pathways, sanitation interventions can be tailored to those pathways with greatest potential public health impact. The SaniPath Exposure Assessment Tool allows users to identify and quantify human exposure to fecal contamination in low-resource urban settings through a systematic yet customizable process. The Tool includes: a project management platform; mobile data collection and a data repository; protocols for primary data collection; and automated exposure assessment analysis. The data collection protocols detail the process of conducting behavioral surveys with households, school children, and community groups to quantify contact with fecal exposure pathways and of collecting and analyzing environmental samples for E. coli as an indicator of fecal contamination. Bayesian analyses are used to estimate the percentage of the population exposed and the mean dose of fecal exposure from microbiological and behavioral data. Fecal exposure from nine pathways (drinking water, bathing water, surface water, ocean water, open drains, floodwater, raw produce, street food, and public or shared toilets) can be compared through a common metric-estimated ingestion of E. coli units (MPN or CFU) per month. The Tool generates data visualizations and recommendations for interventions designed for both scientific and lay audiences. When piloted in Accra, Ghana, the results of the Tool were comparable with that of an in-depth study conducted in the same neighborhoods and highlighted consumption of raw produce as a dominant exposure pathway. The Tool has been deployed in nine cities to date, and the results are being used by local authorities to design and prioritize programming and policy. The SaniPath Tool is a novel approach to support public-health evidence-based decision-making for urban sanitation policies and investments.

Heavy metals in the Arctic: Distribution and enrichment of five metals in Alaskan soils

Metal contamination of food and water resources is a known public health issue in Arctic and sub-Arctic communities due to the proximity of many communities to mining and drilling sites. In addition, permafrost thaw may release heavy metals sequestered in previously frozen soils, potentially contaminating food and water resources by increasing the concentration of metals in freshwater, plants, and wildlife. Here we assess the enrichment of selected heavy metals in Alaskan soils by synthesizing publicly available data of soil metal concentrations. We analyzed data of soil concentrations of arsenic, chromium, mercury, nickel, and lead from over 1,000 samples available through the USGS Alaskan Geochemical Database to evaluate 1) the spatial distribution of sampling locations for soil metal analysis, 2) metal concentrations in soils from different land cover types and depths, and 3) the occurrence of soils in Alaska with elevated metal concentrations relative to other soils. We found substantial clustering of sample sites in the southwestern portion of Alaska in discontinuous and sporadic permafrost, while the continuous permafrost zone in Northern Alaska and the more populous Interior are severely understudied. Metal concentration varied by land cover type but lacked consistent patterns. Concentrations of chromium, mercury, and lead were higher in soils below 10 cm depth, however these deeper soils are under-sampled. Arsenic, chromium, mercury, nickel and lead concentrations exceeded average values for US soils by one standard deviation or more in 3.7% to 18.7% of the samples in this dataset. Our analysis highlights critical gaps that impede understanding of how heavy metals in thawing permafrost soils may become mobilized and increase exposure risk for Arctic communities.

Opportunities for evaluating chemical exposures and child health in the United States: the Environmental influences on Child Health Outcomes (ECHO) Program

The Environmental Influences on Child Health Outcomes (ECHO) Program will evaluate environmental factors affecting children's health (perinatal, neurodevelopmental, obesity, respiratory, and positive health outcomes) by pooling cohorts composed of >50,000 children in the largest US study of its kind. Our objective was to identify opportunities for studying chemicals and child health using existing or future ECHO chemical exposure data. We described chemical-related information collected by ECHO cohorts and reviewed ECHO-relevant literature on exposure routes, sources, and environmental and human monitoring. Fifty-six ECHO cohorts have existing or planned chemical biomonitoring data for mothers or children. Environmental phenols/parabens, phthalates, metals/metalloids, and tobacco biomarkers are each being measured by ≥15 cohorts, predominantly during pregnancy and childhood, indicating ample opportunities to study child health outcomes. Cohorts are collecting questionnaire data on multiple exposure sources and conducting environmental monitoring including air, dust, and water sample collection that could be used for exposure assessment studies. To supplement existing chemical data, we recommend biomonitoring of emerging chemicals, nontargeted analysis to identify novel chemicals, and expanded measurement of chemicals in alternative biological matrices and dust samples. ECHO's rich data and samples represent an unprecedented opportunity to accelerate environmental chemical research to improve the health of US children.

An integrated interdisciplinary approach to evaluate potentially toxic element sources in a mountainous watershed

Potentially toxic elements (PTEs, i.e., Cd, Ni, Cr) and their source apportionment in waters are of major environmental concern. Different approaches can be used to evaluate PTEs sources in environment, but single-way approaches are often limited and can easily fail. PTEs sources apportionment should include the evaluation of geochemical background and spatiotemporal trends analyses. We propose an integrated approach, and we apply it to a mountain catchment in the Italian central Alps, where ultramafic terranes crop out. We collected water and glacial sediment samples during the melting season. Then, we analyzed major ions and PTEs in waters, and we quantified the total PTEs load in sediments through acid digestion. Data were then processed through spatial and temporal trends analysis, clustering of variables and the evaluation of partition between the different compartments. We found a high geochemical background of part of the PTEs, consistently with results from other areas worldwide on mafic and ultramafic terranes (high concentrations of Ni, Cr and Fe), while we identified an additional atmospheric deposition source for Zn, Cd and Ag. Also, redundant observations on Cu, As and Pb indicated a possible mixed source. This study elucidates the need for an integrated approach to avoid unnecessary or misleading assumptions in the PTE's source appointment. A single-way approach application, in fact, can fail in understanding element source in a complicated and dynamic compartment like surface water.

The southern Gulf of Mexico: A baseline radiocarbon isoscape of surface sediments and isotopic excursions at depth

The southern Gulf of Mexico (sGoM) is home to an extensive oil recovery and development infrastructure. In addition, the basin harbors sites of submarine hydrocarbon seepage and receives terrestrial inputs from bordering rivers. We used stable carbon, nitrogen, and radiocarbon analyses of bulk sediment organic matter to define the current baseline isoscapes of surface sediments in the sGoM and determined which factors might influence them. These baseline surface isoscapes will be useful for accessing future environmental impacts. We also examined the region for influence of hydrocarbon deposition in the sedimentary record that might be associated with hydrocarbon recovery, spillage and seepage, as was found in the northern Gulf of Mexico (nGoM) following the Deepwater Horizon (DWH) oil spill in 2010. In 1979, the sGoM experienced a major oil spill, Ixtoc 1. Surface sediment δ13C values ranged from -22.4‰ to -19.9‰, while Δ14C values ranged from -337.1‰ to -69.2‰. Sediment δ15N values ranged from 2.8‰ to 7.2‰, while the %C on a carbonate-free basis ranged in value of 0.65% to 3.89% and %N ranged in value of 0.09% to 0.49%. Spatial trends for δ13C and Δ14C were driven by water depth and distance from the coastline, while spatial trends for δ15N were driven by location (latitude and longitude). Location and distance from the coastline were significantly correlated with %C and %N. At depth in two of twenty (10%) core profiles, we found negative δ13C and Δ14C excursions from baseline values in bulk sedimentary organic material, consistent with either oil-residue deposition or terrestrial inputs, but likely the latter. We then used 210Pb dating on those two profiles to determine the time in which the excursion-containing horizons were deposited. Despite the large spill in 1979, no evidence of hydrocarbon residue remained in the sediments from this specific time period.

Land use change affects water erosion in the Nepal Himalayas

Soil erosion is a global environmental threat, and Land Use Land Cover Changes (LUCC) have significant impacts on it. Nepal, being a mountainous country, has significant soil erosion issues. To examine the effects of LUCC on water erosion, we studied the LUCC in Sarada, Rapti and Thuli Bheri river basins of Nepal during the 1995-2015 period using the Remote Sensing. We calculated the average annual soil loss using the Revised Universal Soil Loss Equation and Geographical Information System. Our results suggest that an increase in the agricultural lands at the expense of bare lands and forests escalated the soil erosion through the years; rates being 5.35, 5.47 and 6.03 t/ha/year in 1995, 2007 and 2015, respectively. Of the different land uses, agricultural land experienced the most erosion, whereas the forests experienced the least erosion. Agricultural lands, particularly those on the steeper slopes, were severely degraded and needed urgent soil and water conservation measures. Our study confirms that the long term LUCC has considerable impacts on soil loss, and these results can be implemented in similar river basins in other parts of the country.

Air Quality Index and Respiratory Symptoms among Garri Processing Workers in Ogbomoso, Nigeria

BACKGROUND

Epidemiological studies have linked morbidity and mortality of individuals to exposure to atmospheric gaseous and particulate matter especially fine particles (PM2.5) and coarse particles (PM10).The process of garri (cassava crisps) production (frying with firewood) is associated with production of gaseous and particulate matter which contribute to ambient particulate matter air pollution. The objective of this study was to determine the impact of air quality indices on the prevalence of respiratory symptoms among the garri processing workers in Ogbomoso.

METHODOLOGY

A comparative cross-sectional study was carried out among 351 garri processing workers in Ogbomoso as subjects and 351 residents of Ogbomoso metropolis as controls that were age, sex and height-matched. A semi-structured questionnaire was used to obtain sociodemographic profiles and respiratory symptoms of participants and document physical examination findings. Particulate matter counter was used for air quality sampling.

RESULTS

The mean age of subjects was 41.7 ± 14.9 years and that of controls was 41.6 ± 14.7 years (p =0.960). The two groups were also matched for socioeconomic status, sex and height. Prevalence of respiratory symptoms was higher at 48.4% among the garri workers than the control group which was 29.1% (p<0.001). Cough was the predominant symptom with a prevalence of 29.3% and 10.5% among the subjects and controls respectively. The mean count of PM1.0 in garri processing locations was significantly higher than that recorded in Ogbomoso metropolis; 73.77±42.08 vs 15.04±6.66mg/m3 (p<0.0001).

CONCLUSION

Garri processing work is associated with significant increase in ambient air pollution and increased prevalence of respiratory symptoms among the workforce compared to the control population. Effective preventive strategies including education and provision of safety masks may reduce the occupational hazards associated with garri processing factory workers in Nigeria.

Comparing spatial regression to random forests for large environmental data sets

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records that are spatially autocorrelated. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. A primary application is mapping MMI predictions and prediction errors at 1.1 million perennial stream reaches across the conterminous United States. For the spatial regression model, we develop a novel transformation procedure that estimates Box-Cox transformations to linearize covariate relationships and handles possibly zero-inflated covariates. We find that the spatial regression model with transformations, and a subsequent selection of significant covariates, has cross-validation performance comparable to random forests. We also find that prediction interval coverage is close to nominal for each method, but that spatial regression prediction intervals tend to be narrower and have less variability than quantile regression forest prediction intervals. A simulation study is used to generalize results and clarify advantages of each modeling approach.

Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models

Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl^-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu.

Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression

National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM₁₀, PM_2.5, NO₂, SO₂, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R² conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.

Quality criteria for groundwater use from a rural part of Wanaparthy District, Telangana State, India, through ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA)

The present study region comprises granite and granite gneisses aquifer system constituted by Precambrian rocks. Groundwater is the primary source for drinking and other domestic purposes. Many developing regions in the world suffer from lack of safe drinking water. A rural part of Wanaparthy District in Telangana State, India, is one of them. For this reason, the groundwater samples collected from the study region were analyzed for pH, TDS, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, SO₄^2-, NO₃^- and F^- and evaluated groundwater quality criteria, using ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA). The ISD maps show that some locations are not suitable for drinking purpose due to exceeding concentrations of TDS, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, NO₃^-and F^-, compared to those with national drinking water quality standards. According to the EWQI, about 3%, 47%, 43% and 7% of the total area come under the excellent, good, medium and extremely poor water quality types for drinking purpose, respectively. Chadha's diagram classified the area as carbonate hardness (63%), non-carbonate alkali (17%), carbonates alkali (13%) and non-carbonate hardness (7%) zones. The binary diagrams (Na⁺ + K⁺ vs TC, Na⁺ vs Ca²⁺ and HCO₃^- vs TC) indicate that the quality of groundwater is controlled by influences of water-rock interactions, mineral weathering and dissolution, ion exchange and evaporation as well as the impact of anthropogenic sources. The PCA transferred the chemical variables into three principal components accounts for about 81% of the total variance. The high positive loadings of PC1 (Cl^-, TDS, SO₄^2-, Na⁺, NO₃^-, Mg²⁺ and HCO₃^-) stand for processes of silicate weathering and dissolution, ion exchange and evaporation, and the influence of domestic waste waters, irrigation return flows and chemical fertilizers on the groundwater system, the PC2 (F^- and pH) signifies the alkaline nature of groundwater, which causes fluorosis, and the PC3 (K⁺) is a result of potassium fertilizers. The study helps to take remediate measures at a specific site and hence suggests the treatment of water before its drinking and also the recharge of the aquifer artificially to improve the groundwater quality.

Changes in ocean health in British Columbia from 2001 to 2016

Effective management of marine systems requires quantitative tools that can assess the state of the marine social-ecological system and are responsive to management actions and pressures. We applied the Ocean Health Index (OHI) framework to retrospectively assess ocean health in British Columbia annually from 2001 to 2016 for eight goals that represent the values of British Columbia's coastal communities. We found overall ocean health improved over the study period, from 75 (out of 100) in 2001 to 83 in 2016, with scores for inhabited regions ranging from 68 (North Coast, 2002) to 87 (West Vancouver Island, 2011). Highest-scoring goals were Tourism & Recreation (average 94 over the period) and Habitat Services (100); lowest-scoring goals were Sense of Place (61) and Food Provision (64). Significant increases in scores over the time period occurred for Food Provision (+1.7 per year), Sense of Place (+1.4 per year), and Coastal Livelihoods (+0.6 per year), while Habitat Services (-0.01 per year) and Biodiversity (-0.09 per year) showed modest but statistically significant declines. From the results of our time-series analysis, we used the OHI framework to evaluate impacts of a range of management actions. Despite challenges in data availability, we found evidence for the ability of management to reduce pressures on several goals, suggesting the potential of OHI as a tool for assessing the effectiveness of marine resource management to improve ocean health. Our OHI assessment provides an important comprehensive evaluation of ocean health in British Columbia, and our open and transparent process highlights opportunities for improving accessibility of social and ecological data to inform future assessment and management of ocean health.

Accuracy of long-term volunteer water monitoring data: A multiscale analysis from a statewide citizen science program

An increasing number of citizen science water monitoring programs is continuously collecting water quality data on streams throughout the United States. Operating under quality assurance protocols, this type of monitoring data can be extremely valuable for scientists and professional agencies, but in some cases has been of limited use due to concerns about the accuracy of data collected by volunteers. Although a growing body of studies attempts to address accuracy concerns by comparing volunteer data to professional data, rarely has this been conducted with large-scale datasets generated by citizen scientists. This study assesses the relative accuracy of volunteer water quality data collected by the Texas Stream Team (TST) citizen science program from 1992–2016 across the State of Texas by comparing it to professional data from corresponding stations during the same time period. Use of existing data meant that sampling times and protocols were not controlled for, thus professional and volunteer comparisons were refined to samples collected at stations within 60 meters of one another and during the same year. Results from the statewide TST dataset include 82 separate station/year ANOVAs and demonstrate that large-scale, existing volunteer and professional data with unpaired samples can show agreement of ~80% for all analyzed parameters (DO = 77%, pH = 79%, conductivity = 85%). In addition, to assess whether limiting variation within the source datasets increased the level of agreement between volunteers and professionals, data were analyzed at a local scale. Data from a single partner city, with increased controls on sampling times and locations and correction of a systematic bias in DO, confirmed this by showing an even greater agreement of 91% overall from 2009–2017 (DO = 91%, pH = 83%, conductivity = 100%). An experimental sampling dataset was analyzed and yielded similar results, indicating that existing datasets can be as accurate as experimental datasets designed with researcher supervision. Our findings underscore the reliability of large-scale citizen science monitoring datasets already in existence, and their potential value to scientific research and water management programs.

Determining hydrological flow paths to enhance restoration in impaired mangrove wetlands

The restoration of mangroves has gained prominence in recent decades. Hydrological rehabilitation has been undertaken to connect impaired mangroves with the sea, lagoons or estuaries. Because mangrove hydrodynamics occurs on the surface and interstitial spaces in the sediment, we propose to determine the hydrological flow paths to restore the hydrological regimes of the impaired mangroves. The hydrological flow paths were determined through a micro basin analysis based on microtopographic data to generate a digital elevation model. Applying this methodological approach, the hydrology of an impaired area on a barrier island in the Gulf of Mexico was restored by excavating, desilting or clearing the channels on the identified hydrological flow paths. This area was compared to one in which impaired mangroves were reconnected to the marine lagoon but disregarding the flow paths. Data collected in both areas were evaluated by flood level analysis, using two methods: (i) a simple linear regression model (SLRM) and (ii) spectral analysis (SA), also known as dominant frequency analysis. The results suggest that restoration based on the hydrological flow paths was more effective than the direct opening to the nearest main water body without accounting for the microtopography. In both areas, soil salinity and sulfides decreased after hydrological reconnection. However, a greater efficiency in the investment of time and human resources was achieved when preferential flow paths were identified and taken into account. The methodological procedures described in this study are of universal application to other mangrove restoration programs.

Hyper-spectral response and estimation model of soil degradation in Kenli County, the Yellow River Delta

The ecological environment of the Yellow River Delta is fragile, and the soil degradation in the region is serious. Therefore it is important to discern the status of the soil degradation in a timely manner for soil conservation and utilization. The study area of this study was Kenli County in the Yellow River Delta of China. First, physical and chemical data of the soil were obtained by field investigations and soil sample analyses, and the hyper-spectra of air-dried soil samples were obtained via spectrometer. Then, the soil degradation index (SDI) was constructed by the key indicators of soil degradation, including pH, SSC, OM, AN, AP, AK, and soil texture. Next, according to a cluster analysis, soil degradation was divided into the following three grades: light degradation, moderate degradation, and heavy degradation. Moreover, the spectral characteristics of soil degradation were analyzed, and an estimation model of SDI was established by multiple stepwise regression. The results showed that the overall level of reflectance spectra increased with increased degree of soil degradation, that both derivative transformation and waveband reorganization could enhance the spectral information of soil degradation, and that the correlation between SDI and the spectral parameter of (R_λ2+R_λ1)/(R_λ2-R_λ1) was the highest among all the spectral parameters studied. On this basis, the optimum estimation model of SDI was established with the correlation coefficient of 0.811. This study fully embodies the potential of hyper-spectral technology in the study of soil degradation and provides a technical reference for the rapid extraction of information from soil degradation. Additionally, the study area is typical and representative, and thus can indirectly reflect the soil degradation situation of the whole Yellow River Delta.

An Assessment of the Performance of the PLUS+ Tool in Supporting the Evaluation of Water Framework Directive Compliance in Scottish Standing Waters

Phosphorus is one of the main causes of waterbodies in Scotland being at less than good ecological status (GES) in terms of the water framework directive (WFD). In Scotland, there are more than 8000 standing waters, defined as lakes and reservoirs that have a surface area of more than 1 hectare. Only about 330 of these are monitored routinely to assess compliance with the WFD. The export coefficient tool PLUS+ (phosphorus land use and slope) has been developed to estimate total phosphorus (TP) concentrations in the unmonitored sites; modelled values are then compared to WFD target concentrations for high, good, moderate, poor, and bad status to assess compliance. These type-specific or site-specific targets are set by the regulatory authority and form part of a suite of physical, chemical, and ecological targets that are used to assess GES, all of which must be met. During development, the PLUS+ tool was applied to 323 monitored catchments and 7471 unmonitored catchments. The efficacy of the tool was assessed against TP concentrations observed in 2014 and found to perform well in the rural catchments. 51% of standing waters had the same modelled and observed WFD class (i.e., High, Good, Moderate, Poor, Bad), and a further 40% of standing waters had a modelled WFD class that was within one class of observed water quality. The tool performed less well in catchments with larger inputs of TP from urban sources (e.g., sewage). The greatest deviations between measured and modelled classes were explained by the shortage of information on wastewater treatment works, fish farms, migratory birds, levels of uncertainty in TP measurements, and the amount of in-lake re-cycling of P. The limitations of the tool are assessed using data from six well documented case study sites and recommendations for improving the model performance are proposed.

Eutrophication Assessment Based on the Cloud Matter Element Model

Eutrophication has become one of the most serious problems threatening the lakes/reservoirs in China over 50 years. Evaluation of eutrophication is a multi-criteria decision-making process with uncertainties. In this study, a cloud matter element (CME) model was developed in order to evaluate eutrophication level objectively and scientifically, which incorporated the randomness and fuzziness of eutrophication evaluation process. The elements belonging to each eutrophication level in the CME model were determined by means of certainty degrees through repeated simulations of cloud model with reasonable parameters of expectation E_x, entropy E_n, and hyper-entropy H_e. The weights of evaluation indicators were decided by a combination of entropy technology and analytic hierarchy process method. The neartudes of water samples to each eutrophication level of lakes/reservoirs in the CME model were generated and the eutrophication levels were determined by maximum neartude principal. The proposed CME model was applied to evaluate eutrophication levels of 24 typical lakes/reservoirs in China. The results of the CME model were compared with those of comprehensive index method, matter element model, fuzzy matter element model, and cloud model. Most of the results obtained by the CME model were consistent with the results obtained by other methods, which proved the CME model is an effective tool to evaluate eutrophication.

Fate and partitioning of heavy metals in soils from landfill sites in Cape Town, South Africa: a health risk approach to data interpretation

The fate and persistence of trace metals in soils and sludge from landfill sites are crucial in determining the hazard posed by landfill, techniques for their restoration and potential reuse purposes of landfill sites after closure and restoration. A modified European Community Bureau of Reference's (BCR) sequential extraction procedure was applied for partitioning and evaluating the mobility and persistence of trace metals (As, Cd, Cr, Cu, Ni, Pb, Sb, Se, Zn) in soils from three landfill sites and sludge sample from Cape Town, South Africa. Inductively coupled plasma optical emission spectroscopy was used to analyze BCR extracts. The mobility sequence based on the BCR mobile fraction showed that Cu (74-87%), Pb (65-80%), Zn (59-82%) and Cd (55-66%) constituted the mobile metals in the soils from the three sites. The mobility of Cu, Zn and Ni (> 95%) was particularly high in the sludge sample, which showed significant enrichment compared to the soil samples. Geo-accumulation index (I_geo) and risk assessment code were used to further assess the environmental risk of the metals in the soils. Exposure to the soils and sludge did not pose any non-cancer risks to adult and children as the hazard quotient and hazard index values were all below the safe level of 1. The cancer risks from Cd, Cr and Ni require that remedial action be considered during closure and restoration of the landfill sites.

Source and background threshold values of potentially toxic elements in soils by multivariate statistics and GIS-based mapping: a high density sampling survey in the Parauapebas basin, Brazilian Amazon

A high-density regional-scale soil geochemical survey comprising 727 samples (one sample per each 5 × 5 km grid) was carried out in the Parauapebas sub-basin of the Brazilian Amazonia, under the Itacaiúnas Basin Geochemical Mapping and Background Project. Samples were taken from two depths at each site: surface soil, 0-20 cm and deep soil, 30-50 cm. The ground and sieved (< 75 µm) fraction was digested using aqua regia and analyzed for 51 elements by inductively coupled plasma mass spectrometry (ICPMS). All data were used here, but the principal focus was on the potential toxic elements (PTEs) and Fe and Mn to evaluate the spatial distribution patterns and to establish their geochemical background concentrations in soils. Geochemical maps as well as principal component analysis (PCA) show that the distribution patterns of the elements are very similar between surface and deep soils. The PCA, applied on clr-transformed data, identified four major associations: Fe-Ti-V-Sc-Cu-Cr-Ni (Gp-1); Zr-Hf-U-Nb-Th-Al-P-Mo-Ga (Gp-2); K-Na-Ca-Mg-Ba-Rb-Sr (Gp-3); and La-Ce-Co-Mn-Y-Zn-Cd (Gp-4). Moreover, the distribution patterns of elements varied significantly among the three major geological domains. The whole data indicate a strong imprint of local geological setting in the geochemical associations and point to a dominant geogenic origin for the analyzed elements. Copper and Fe in Gp-1 were enriched in the Carajás basin and are associated with metavolcanic rocks and banded-iron formations, respectively. However, the spatial distribution of Cu is also highly influenced by two hydrothermal mineralized copper belts. Ni-Cr in Gp-1 are highly correlated and spatially associated with mafic and ultramafic units. The Gp-2 is partially composed of high field strength elements (Zr, Hf, Nb, U, Th) that could be linked to occurrences of A-type Neoarchean granites. The Gp-3 elements are mobile elements which are commonly found in feldspars and other rock-forming minerals being liberated by chemical weathering. The background threshold values (BTV) were estimated separately for surface and deep soils using different methods. The '75th percentile', which commonly used for the estimation of the quality reference values (QRVs) following the Brazilian regulation, gave more restrictive or conservative (low) BTVs, while the 'M_MAD' was more realistic to define high BTVs that can better represent the so-called mineralized/normal background. Compared with CONAMA Resolution (No. 420/2009), the conservative BTVs of most of the toxic elements were below the prevention limits (PV), except Cu, but when the high BTVs are considered, Cu, Co, Cr and Ni exceeded the PV limits. The degree of contamination (C_deg), based on the conservative BTVs, indicates low contamination, except in the Carajás basin, which shows many anomalies and had high contamination mainly from Cu, Cr and Ni, but this is similar between surface and deep soils indicating that the observed high anomalies are strictly related to geogenic control. This is supported when the C_deg is calculated using the high BTVs, which indicates low contamination. This suggests that the use of only conservative BTVs for the entire region might overestimate the significance of anthropogenic contamination; thus, we suggest the use of high BTVs for effective assessment of soil contamination in this region. The methodology and results of this study may help developing strategies for geochemical mapping in other Carajás soils or in other Amazonian soils with similar characteristics.

Levels, sources, and toxicity assessment of polycyclic aromatic hydrocarbons in urban topsoils of an intensively developing Western Siberian city

This study investigates the levels, sources, spatial distribution, and toxicity of polycyclic aromatic hydrocarbons (PAHs) in urban soils of Tyumen, Russia. Observations of PAHs in cities of Western Siberia accomplished by a representative set of samples are very rare, even though it is one of the most urbanized parts of Russia. Therefore, it is important to estimate the status of PAHs in soils of urban environments representing vast Siberian regions. Tyumen, as one of the most intensively developing cities of Western Siberia, is a good object for such studies. Topsoil samples (0-10 cm) were collected from 241 sampling sites on a regular grid within Tyumen city limits. It was found that total concentration of 12 priority PAHs ranged from 33.4 to 2147.9 μg kg^-1, with a median value of 280.3 μg kg^-1. High-molecular-weight (HMW) PAHs were dominant, accounting for the majority (62%) of the total PAHs. Among the PAHs in soils, 4-ring compounds were predominant in all studied samples, followed by 5- and 6-ring PAHs. Phenanthrene, pyrene, fluoranthene, and benzo(ghi)perylene had values of 28%, 19%, 15%, and 10% of total PAHs, respectively. Results showed that large high-contrast anomalies of HMW PAHs were related to low-residential and transport areas. The diagnostic ratios, as well as hierarchical cluster analysis (HCA) and principal component analysis (PCA), confirmed that sources associated with the transport were the most significant, while biomass combustion played an important role as a source of PAHs in agricultural, low-residential, and recreational areas. Levels of individual PAHs, as well as total PAHs in urban soils of Tyumen, were comparable with those in non-industrial cities with similar populations but were significantly lower than those in large industrial centers. Assessment of soil toxicity using toxic equivalent quantity (TEQ_BaP) showed that the TEQ_BaP values varied from 2.0 to 388.2 μg kg^-1, with a mean value of 34.9 μg kg^-1 and median of 19.8 μg kg^-1, and were lower than those in heavy industrialized cities, but higher than those in soils of cities specializing in consumer and service industries.