Use of statistical and GIS techniques to assess and predict concentrations of heavy metals in soils of Lahore City, Pakistan

Uncovering the impact of mega-scale shipbreaking yards on soil and crop quality in Bangladesh: A spatiotemporal dynamics and associated health risks of metal/loid contamination

The uncontrolled release of harmful metal/loids from mega-scale shipbreaking activities in Bangladesh is a significant concern. This study investigated the impact of shipbreaking activities on soil and crop quality and human health in relation to metal/loid contamination. This work covered an area of 1221 km2 surrounding the shipbreaking yards in Chittagong during the wet and dry seasons between 2019 and 2020. Amongst the sixteen elements measured, the concentrations of Pb, Cd, As, V, Cr, Mn, Cu, Zn, Fe, Co, Ni, and Sn in the soil, rice, and vegetables from the four exposure sites were significantly higher compared to the control site in both seasons. Soil pollution indices indicated moderate to higher contamination levels of Pb, Zn, Cd, As, and Se in 30-50% of soil, supporting their accumulation in food crops. Source apportionment analysis identified uncontrolled shipwrecking operations as the primary anthropogenic activity mainly contributing to metal/loid pollution. Health risk analysis showed inorganic arsenic (estimated), Cd, and Pb in food crops could pose potential health threats to the general population. Spinach leaf and gourd were identified as the highest-risk contributing vegetables in the dry and wet seasons. These findings help to inform management strategies to protect agroecosystems and public health.

Health risk assessment and geospatial analysis of arsenic contamination in shallow aquifer along Ravi River, Lahore, Pakistan

The exposure variation of arsenic from different ground and surface water sources has remained unpredictable which may cause severe human health problems. The current study is, therefore, designed to analyze the spatial variability of arsenic contamination in shallow aquifer and assess the potential human health risks. For this purpose, a total of 55 groundwater, 10 drain water, 4 river water, and 6 sediment samples were collected along zero to 5 km stretch of the River Ravi, Lahore. All water samples were tested for As, pH, and total dissolved solids (TDS), whereas sediments were only tested for As. Health risk models were used to predict cancer and non-cancer risk in adults and children. Among water samples, highest median (minimum-maximum) concentrations (µg/L) of As were recorded 53.32 (1.98-1555) in groundwater, followed by 53.04 (1.58-351.5) in drain water, and 4.80 (2.13-8.67) in river water, respectively, whereas As concentration (mg/kg) in river sediments was 6.03 (5.56-13.92). Variation of As in groundwater was non-significant (P > 0.05) among every 1-km stretch from the Ravi River. However, maximum median concentrations (µg/L) of 60.18 and 60.08 were recorded between 2-3 and 0-1 km from River Ravi, respectively, reflecting possible mixing of river water with shallow aquifers. A very high cancer and non-cancer risk (HI > 1.0 × 10^-4) through groundwater As exposure was predicted for both children and adults. The current study concluded that prevalence of As above WHO prescribed limits in shallow aquifer along the urban stretch of the River Ravi is posing serious health risk to the exposed population.

Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China

Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0–20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions.

Characteristics and health risk assessment of heavy metal contamination from dust collected on household HVAC air filters

Heavy metals associated with airborne particulate matter are detrimental to human health, but risk assessment is difficult due to the technical challenges of determining exposure rates. In houses and other buildings, the heating, ventilation and air conditioning (HVAC) system is equipped with an air filter that captures airborne particulate matter from the indoor air that enters the HVAC system. This study used the air filter dust as a proxy for the heavy metal exposure of children and adults, based on a household study in Kuwait. Air filter dust contained from 12.5 ± 5 mg Co/kg dust to 14 453 ± 5046 mg Fe/kg dust. Houses had high levels of Fe, Al, Zn, and Mn and relatively low concentrations of As and Co. Source apportionment revealed that metals in air filter dust were from natural and anthropogenic sources, including vehicular emissions, fossil fuel combustion, and metals-related industries. The total Hazard Index (HI; Σ exposure routes) for heavy metals was >1 for children and adults. Total cancer risks (TCR; Σ exposure routes) were 5.93 × 10^-3 (95% CI: 5.28 × 10^-3- 6.59 × 10^-3) for children and 5.16 × 10^-3 (95% CI: 4.59 × 10^-3 - 5.73 × 10^-3) for adults. Heavy metals, particularly the Cr and Pb concentrations, contribute to the non-carcinogenic and carcinogenic health risks of children and adults in Kuwait households.

Characterizing pollution indices and children health risk assessment of potentially toxic metal(oid)s in school dust of Lahore, Pakistan

Toxic metal pollution is a renowned environmental concern, especially to sensitive environments like school classrooms and their association with children's health. The study was planned to determine the pollution characteristics of 13 potentially toxic metal (oid)s (PTMs) and their associated children's health risk assessment from school dust samples of considerably three land-use types (residential, roadside, and industrial areas) of Lahore, Pakistan. Geo-accumulation (I_geo), pollution (PI), integrated pollution (IPI) and pollution load (PLI) indexes were used to determine the PTMs contamination and USEPA health risk assessment models were employed to assess the health risks in children. The mean concentrations of Cd, Cr, Cu, Ni, Pb, and Zn for three land-use types were found much higher than the permissible limits. Results of pollution indices revealed that school dust was strongly contaminated with Cd, Pb, and Zn whilst moderately contaminated with Cr and Cu. Moreover, the health risk assessment models revealed no significant non-cancerous risks in children with predominantly highest hazardous index (HI) of Cr in industrial (4.61E-01) and Pb in both roadside (4.30E-01) and residential (2.26E-01) area schools. According to cumulative HI of all PTMs and exposure routes, the land-use areas were in descending order as industrial > roadside > residential. The calculations of hazardous quotient (HQ) showed ingestion was the leading pathway of PTMs exposure through school dust. For carcinogenic health risk (CR), the most prominent PTM was Cr with values of 1.53E-06 in industrial area schools, found close to the tolerable range (1.0E-06). Hence, school dust of Lahore prominently contaminated with eminent PTMs triggering slight health risks predominantly by ingestion exposure to children.

Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution

Rapid heavy metal soil surveys at large scale with high sampling density could not be conducted with traditional laboratory physical and chemical analyses because of the high cost, low efficiency and heavy workload involved. This study explored a rapid approach to assess heavy metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were successfully predicted by PXRF (R² >0.90 and RPD >2.50) while As and Ni were predicted with less accuracy (R² <0.75 and RPD <1.40). The pH values were well predicted by PVNIR. Classification of heavy metals contamination grades in farmland soils was conducted based on previous results; the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was an effective and rapid method to determine the degree of pollution with soil heavy metals. This study provides a new approach to assess soil heavy metals pollution; this method will facilitate large-scale surveys of soil heavy metal pollution.