Effects of groundwater metal contaminant spatial distribution on overlaying kriged maps

Prophet time series modeling of waste disposal rates in four North American cities

In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.

Hydrogeochemical evolution processes, groundwater quality, and non-carcinogenic risk assessment of nitrate-enriched groundwater to human health in different seasons in the Hawler (Erbil) and Bnaslawa Urbans, Iraq

The main objectives of this research are to assess groundwater, a primary source of drinking water in the urban areas of Hawler (Erbil) and Bnaslawa in northern Iraq, and the non-carcinogenic human health risks of nitrate contamination associated with drinking water quality. For this purpose, twenty-seven groundwater samples were collected from wells to assess the hydrogeochemical characteristics and groundwater quality for both natural and anthropogenic purposes during the wet (May 2020) and dry (September 2020) seasons. During the wet and dry seasons, NO3- in groundwater ranged from 14.00 to 61.00 mg/L and 12.00 to 60.00 mg/L, with an average value of 35.70 and 29.00 mg/L, respectively. Approximately 25.92% of the samples exceeded the permissible limit of the WHO (2011) drinking water standard. The ratios of NO3-/Na+ vs. Cl-/Na+ and SO42-/Na+ vs. NO3-/Na+ indicate the effect of agricultural activities and wastewater leaking from cesspools or septic tanks on the quality of groundwater during the wet and dry seasons. The entropy weighted water quality index method ranked 62.5% and 75% of the urban groundwater as not recommended for drinking, and the remaining samples are moderately suitable in both wet and dry seasons. The non-carcinogenic human health risk assessment displayed that during the wet and dry seasons, 29.6% and 25.9% of adults, 48% and 30% of children, and 48.1% and 29.6% of infants were exposed to increased concentrations of nitrate in groundwater. Due to high nitrate in drinking water, non-carcinogenic human health risk levels vary as infant > child > adults. The main findings obtained from this study can assist policymakers in better understanding the hydrogeochemical properties of groundwater in terms of drinking water safety, thereby facilitating the management of water resources to take the necessary measures.

Variability of the treated biomedical waste disposal behaviours during the COVID lockdowns

Literature review suggests that studies on biomedical waste generation and disposal behaviors in North America are limited. Given the infectious nature of the materials, effective biomedical waste management is vital to the public health and safety of the residents. This study explicitly examines seasonal variations of treated biomedical waste (TBMW) disposal rates in the City of Regina, Canada, from 2013 to 2022. Immediately before the onset of COVID-19, the City exhibited a steady pattern of TBMW disposal rate at about 6.6 kg∙capita-1∙year-1. However, the COVID-19 pandemic and its associated lockdowns brought about an abrupt and persistent decline in TBMW disposal rates. Inconsistent fluctuations in both magnitude and variability of the monthly TBMW load weights were also observed. The TBMW load weight became particularly variable in 2020, with an interquartile range 4 times higher than 2019. The average TBMW load weight was also the lowest (5.1 tonnes∙month-1∙truckload-1) in 2020, possibly due to an overall decline in non-COVID-19 medical emergencies, cancellation of elective surgeries, and availability of telehealth options to residents. In general, the TBMW disposal rates peaked during the summer and fall seasons. The day-to-day TBMW disposal contribution patterns between the pre-pandemic and post-pandemic are similar, with 97.5% of total TBMW being disposed of on fixed days. Results from this Canadian case study indicate that there were observable temporal changes in TBMW disposal behaviors during and after the COVID-19 lockdowns.

Ranking Canadian waste management system efficiencies using three waste performance indicators

Three waste management system (WMS) efficiency indicators are adopted to systematically assess WMS efficiency in Canada from 1998 to 2016. The study objectives are to examine the temporal changes in waste diversion activities and rank the performance of the jurisdictions using a qualitative analytical framework. Increasing Waste Management Output Index (WMOI) trends were identified in all jurisdictions, and more government subsidiaries and incentive packages are recommended. With the exception of Nova Scotia, statistically significant decreasing diversion gross domestic product (DGDP) ratio trends are observed. It appears that the increases in GDP from Sector 562 were not contributing to waste diversion. On average, Canada spent about $225/tonne of waste handled during the study period. Current spending per tonne handled (CuPT) trends are decreasing, with S ranging from + 5.15 to + 7.67. It appears that WMSs in Saskatchewan and Alberta are more efficient. The results suggest that the use of diversion rate alone to evaluate WMS may be misleading. The findings help the waste community to better understand the trade-offs between various waste management alternatives. The proposed qualitative framework utilizing comparative rankings is applicable elsewhere and can be a useful decision support tool for policy-makers.

Health Risk Assessment of Heavy Metals in Shallow Groundwater of Coal-Poultry Farming Districts

This study aimed to assess the heavy metal (Mn, Ni, Cu, Zn, Sr, Cd, Pb, and Cr) pollution characteristics, sources, and human health risks in shallow groundwater in the impact zones of urban and rural semi-intensive poultry farms in Suzhou City. Ordinary kriging interpolation showed that poultry farming contributed substantially to the pollution of shallow groundwater by Mn, Zn, and Cu. Positive matrix factorization was applied to identify the sources of heavy metals, and the health risks were assessed based on the hazard index and carcinogenic risks of the various sources. Heavy metal enrichment was closely related to anthropogenic activities. In addition, four sources were identified: poultry manure (29.33%), natural source (27.94%), industrial activities (22.29%), and poultry wastewater (20.48%). The main exposure route of carcinogenic and non-carcinogenic risks to adults and children was oral ingestion. The non-carcinogenic risk of oral ingestion in children was higher than that in adults; the carcinogenic risk was higher in adults than in children. Poultry manure (42.0%) was considered the largest contributor to non-carcinogenic risk, followed by poultry wastewater (21%), industrial activities (20%), and natural sources (17%). Industrial activity (44%) was the primary contributor to carcinogenic risk, followed by poultry wastewater (25%), poultry manure (19%), and natural sources (12%).

Hydro-geochemical analysis based on entropy and geostatistics model for delineation of anthropogenic ground water pollution for health risks assessment of Dhenkanal district, India

Consumption of poor quality water causes serious human health hazards. Therefore, it is very crucial to investigate factors influencing the quality of groundwater and its suitability for drinking purpose. In the present study, groundwater quality of the Dhenkanal district of Odisha, India was characterized and the spatial distribution of different water quality parameters were analyzed using the multivariate statistics, entropy theory, and geostatistics techniques. In the present study 112 number of groundwater tube well samples were collected from the study area. The entropy theory revealed that SO₄^2-, Mg⁺² and Cl^- were the most influencing parameters. A similar observation was also observed based on the correlation coefficient analysis. Groundwater quality index (GWQI) and entropy-weighted water quality index (EWQI) classifications indicated that 78.57 and 43.75% of the collected groundwater samples were categorized under excellent water quality, whereas, the rest of the samples were varying from good to medium drinking water quality. In addition, the result of EWQI classification offers more realistic assessment than that of GWQIs owing to its high precision, simplicity and without application of artificial weight. The correlation coefficient between Ca⁺² and HCO₃^-, Mg⁺² and PO₄^- were significantly high which might be due the presence of CaHCO₃ and MgPO₄ in the groundwater samples. The GWQI revealed a weak spatial dependence of groundwater quality.

Investigating sources, driving forces and potential health risks of nitrate and fluoride in groundwater of a typical alluvial fan plain

Groundwater of alluvial fan plains is the foremost water source, especially in arid/semiarid regions. Its contaminants are big issues for water supply and public health concern. To reveal the groundwater chemistry, contaminants sources and health threats in alluvial aquifers, 81 groundwaters were collected from a typical alluvial fan plain of northern China for nitrogen, fluoride and major ions analysis. Statistical analysis and hydrochemical diagrams as well as human health risk assessment were performed. Nitrate is widely distributed and 53% of groundwaters exceed the permissible limit with the maximum concentration up to 326 mg/L. The distributions of nitrite, ammonia and fluoride contaminants are sporadic in spatial, and the concentrations of fluoride in groundwaters are slightly beyond the permissible limit of 1 mg/L. The hydrochemical facies shift from HCO₃-Ca or Mixed HCO₃-Na·Ca type to Mixed Cl-Mg·Ca and ClCa type with the increase of nitrate content. Two factors (Factor-1 and Factor-2) are extracted by factor analysis and account 63% of the total variances. The positive loading of F^- and negative loading of NO₃^- on Factor-2 reveal geogenic and anthropogenic origins, respectively. The significant positive loadings of TDS, TH, SO₄^2-, Cl^-, Ca²⁺, Mg²⁺ on Factor-1 reveal the governing mechanisms on groundwater chemistry by intermixed sources of geogenic origins and anthropogenic inputs. Hydrogeochemical evolution in the study area is driven by both water-rock interaction and anthropogenic forces. Anthropogenic inputs/influences are the dominated forces increasing groundwater nitrate content and salinity in the piedmont zone and the residential and industrial zone of the southeastern lower parts, and would pose potential non-carcinogenic risks to various populations via oral intake pathway. Rational measures should be taken to protect groundwater quality out of the threats of anthropogenic pollution. The geogenic fluoride in groundwater would threat the health of children through oral pathway and should be also concerned. CAPSULE: The driving forces of groundwater chemistry in alluvial fan plains were revealed using integrated approach of factor analysis and geostatistical modelling.

Temporal and Spatial Distributions of Waste Facilities and Solid Waste Management Strategies in Rural and Urban Saskatchewan, Canada

Saskatchewan has the highest number of landfills per capita in Canada. Given the lower population density and the skewed spatial population distribution, comprehensive analysis of municipal solid waste management systems in Saskatchewan is inherently difficult. Most of the published waste studies however focus on city-level waste management, and there is a lack of literature with respect to the rural areas. In this study, landfills and transfer stations are examined temporally and spatially using Geographic Information System. Landfills and transfer stations from 2017 and 2020 were plotted against census division land area, annual budget, and population density to study temporal changes. Saskatchewan witnessed a 54% reduction in the number of landfills and a 55% increase in number of transfer stations between 2017 and 2020. The replacement of landfills with transfer stations are more noticeable in divisions 8, 9, and 16. Regression analysis is conducted, and landfill closure operation show no obvious correlation to division land area, annual budget, or population density. Rural division 18, representing Northern Saskatchewan, has approximately 45% of the land area in the province and has the lowest population density. The findings suggest different waste management strategies are required for urban and rural areas. The results of this study will help policy makers to better implement solid waste management strategies in urban and rural areas.

Application of periodic parameters and their effects on the ANN landfill gas modeling

To reach a practical landfill gas management system and to diminish the negative environmental impacts from landfills, accurate methane (CH₄) prediction is essential. In this study, the preprocessing steps including minimizing multicollinearity, removal of outliers, and errors with missing data imputation are applied to enhance the data quality. This study is the first at employing periodic parameters in the two-stage non-linear auto-regressive model with exogenous inputs (NARX) with the aim of providing a convenient and precise approach to predict the daily CH₄ collection rate from a municipal landfill in Regina, SK, Canada. Using a stepwise procedure, various volumes of training data were assessed, and concluded that employing the 3-year training data reduced the mean absolute percentage error (MAPE) of the CH₄ prediction model by 26.97% at the testing stage. The favorable artificial neural network model performance was obtained using the day of the year (DOY) as a sole input of the time series model with MAPE of 2.12% showing its acceptable ability in CH₄ prediction. Using an only DOY-based model is especially remarkable because of its simplicity and high accuracy showing a convenient and effective approach in time landfill gas modeling, particularly for the landfills with no reliable climatic data.

Heavy metal contamination and exposure risk assessment via drinking groundwater in Vehari, Pakistan

The presence of toxic substances in aquifers, particularly potentially toxic heavy metals, is an important environmental and social concern worldwide. These heavy metals are capable to exert many injurious health effects in human beings by intake of drinking metal-contaminated water. However, very little attention is paid towards quantitative and qualitative analysis of groundwater used for drinking purpose in several less-developed countries. Therefore, this study was intended to estimate, for the first time, the heavy metal levels in groundwater/drinking water in District Vehari, Pakistan. A total of 129 groundwater samples were obtained and subjected to analyze heavy metal concentrations (lead, copper, cadmium, nickel, manganese, chromium, iron, and zinc). Moreover, pH, electrical conductivity, temperature, total dissolved solids, and anion (carbonates, chloride, and bicarbonates) and cation (calcium, potassium, sodium, lithium, and barium) contents of groundwater were also determined. It was noticed that the values of several groundwater physicochemical characteristics such as cation contents, alkalinity, chloride concentration, and especially the concentrations of heavy metals such as Pb (93%), Cd (68%), and Fe (100%) were higher than their limit values given by WHO. Principal component analysis separately grouped heavy metals and physicochemical characteristics of groundwater. The risk assessment indices predicted potential carcinogenic risks due to the consumption of metal-rich groundwater, predominantly with Cd (0.0007-0.03). The mean hazard quotient (HQ) values for all the metals were < 1, while Pb showed HQ > 1 envisaging non-carcinogenic risk with the consumption of studied groundwater. The findings of the study emphasized on the need of appropriate approaches to remediate groundwater before being used for drinking purpose.

Monitoring, Diffusion and Source Speculation Model of Urban Soil Pollution

The rapid industrialization of cities has brought many challenges to the environment and resources. Industrial wastes, automobile exhaust, coal combustion soot and other pollutants accumulate in urban soil, and the characteristics of urban soil are changed, causing many pollutants to accumulate in the urban soil environment. Heavy metals are toxic and harmful pollutants existing in soil that cannot be biodegraded or thermally degraded; thus, heavy metals pose a threat to environmental quality and humans. To solve the environmental pollution of soil heavy metals, we utilize kriging interpolation to determine the geological distribution of the environmental pollution of metal elements and analyze the main causes of soil heavy metal pollution. Next, the propagation characteristics and diffusion process of heavy metal pollutants are thoroughly analyzed; in addition, an improved one-dimensional convective dispersion model and an improved air subsidence model are established, and real urban soil data are taken as an example for the fitting test. The results show that the improved models that consider more factors, such as adsorption or decomposition factors during the process of convective dispersion, absorption and expulsion factors of the crop root and topographic factors and height changes during the process of air subsidence, are effective. This paper is helpful for distinguishing the primary pollution sources and migration routes of soil metal element pollution and provides a certain reference value for protecting the environment and reducing heavy metal pollution.

Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation

To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models are required for more precise prediction of the amount and recovery time of methane gas from landfills. When the study associates to greenhouse gas emissions problems, time series prediction models are of considerable interests, in which significant past records of gas data are required. This study is the first to specially impute the missing methane (CH₄) data for applying in time series artificial neural network (ANN) model in an attempt to predict daily CH₄ generation rate from a landfill in Regina, SK, Canada. Pre-processing was conducted on data to evaluate independent and significant meteorological input variables and provide suitable dataset for developing CH₄ generation models. A two-stage time series model proposed in this study was performed by missing data imputation at the first stage, followed by a neural network auto-regressive model with exogenous inputs (NARX) at the second stage. The model with 3 layers, 5 climatic variables and 9 neurons in the hidden layer was the optimal structure. This model shows the high performance in CH₄ prediction with the average index of agreement of 0.92 and the average mean absolute percentage error (MAPE) of 3.03% during the testing stage. Missing data imputation coupled with NARX method decreased the mean squared error (MSE) of the model by 84% (compared to Multilayer Perceptrons neural network model) in the testing period representing the effectiveness of missing data estimation coupling with time series ANN models in daily CH₄ generation prediction.