Avit Kumar Bhowmik

Mapping human health risks from exposure to trace metal contamination of drinking water sources in Pakistan

The consumption of contaminated drinking water is one of the major causes of mortality and many severe diseases in developing countries. The principal drinking water sources in Pakistan, i.e. ground and surface water, are subject to geogenic and anthropogenic trace metal contamination. However, water quality monitoring activities have been limited to a few administrative areas and a nationwide human health risk assessment from trace metal exposure is lacking. Using geographically weighted regression (GWR) and eight relevant spatial predictors, we calculated nationwide human health risk maps by predicting the concentration of 10 trace metals in the drinking water sources of Pakistan and comparing them to guideline values. GWR incorporated local variations of trace metal concentrations into prediction models and hence mitigated effects of large distances between sampled districts due to data scarcity. Predicted concentrations mostly exhibited high accuracy and low uncertainty, and were in good agreement with observed concentrations. Concentrations for Central Pakistan were predicted with higher accuracy than for the North and South. A maximum 150-200 fold exceedance of guideline values was observed for predicted cadmium concentrations in ground water and arsenic concentrations in surface water. In more than 53% (4 and 100% for the lower and upper boundaries of 95% confidence interval (CI)) of the total area of Pakistan, the drinking water was predicted to be at risk of contamination from arsenic, chromium, iron, nickel and lead. The area with elevated risks is inhabited by more than 74 million (8 and 172 million for the lower and upper boundaries of 95% CI) people. Although these predictions require further validation by field monitoring, the results can inform disease mitigation and water resources management regarding potential hot spots.

Pesticide mixtures in streams of several European countries and the USA

Given the multitude of pesticides used in agriculture, adjacent streams are typically exposed to pesticide mixtures. Previous studies analysed the ecological risks of a few pesticide mixtures or were limited to an individual region or crop, whereas a large scale analysis of pesticide mixtures is missing. We analysed routine monitoring data from Germany, France, the Netherlands and the USA comprising a total of 4532 sites and 56,084 sampling occasions with the aim to identify the most frequently detected pesticides, their metabolites and mixtures. The most frequently detected compounds were dominated by herbicides and their metabolites. Mixtures mostly comprised of two up to five compounds, whereas mixtures in the USA and France had clearly less compounds than those of Germany and the Netherlands. The number of detected pesticides and thereby the size of mixtures is positively correlated to the number of measured pesticides (r=0.57). In contrast, a low relationship was found to the ratio of agricultural areas within the catchment (r=0.17), and no relationship was found to the size of the catchment (r=0.06). Overall, our study provides priority mixtures for different countries that may be used for future ecotoxicological studies to improve risk assessment for stream ecosystems.

Human Arsenic exposure via dust across the different ecological zones of Pakistan.

The present study aims to assess the arsenic (As) levels into dust samples and its implications for human health, of four ecological zones of Pakistan, which included northern frozen mountains (FMZ), lower Himalyian wet mountains (WMZ), alluvial riverine plains (ARZ), and low lying agricultural areas (LLZ). Human nail samples (N=180) of general population were also collected from the similar areas and all the samples were analysed by using ICP-MS. In general the higher levels (p<0.05) in paired dust and human nail samples were observed from ARZ and LLZ than those of other mountainous areas (i.e., WMZ and FMZ), respectively. Current results suggested that elevated As concentrations were associated to both natural, (e.g. geogenic influences) and anthropogenic sources. Linear regression model values indicated that As levels into dust samples were associated with altitude (r(2)=0.23), soil carbonate carbon density (SCC; r(2)=0.33), and population density (PD; r(2)=0.25). The relationship of paired dust and nail samples was also investigated and associations were found for As-nail and soil organic carbon density (SOC; r(2)=0.49) and SCC (r(2)=0.19) in each studied zone, evidencing the dust exposure as an important source of arsenic contamination in Pakistan. Risk estimation reflected higher hazard index (HI) values of non-carcinogenic risk (HI>1) for children populations in all areas (except FMZ), and for adults in LLZ (0.74) and ARZ (0.55), suggesting that caution should be paid about the dust exposure. Similarly, carcinogenic risk assessment also highlighted potential threats to the residents of LLZ and ARZ, as in few cases (5-10%) the values exceeded the range of US-EPA threshold limits (10(-6)-10(-4)).

Statistical evaluation of spatial interpolation methods for small-sampled region: a case study of temperature change phenomenon in Bangladesh

This study compares three interpolation methods to create continuous surfaces that describe temperature trends in Bangladesh between years 1948 and 2007. The reviewed techniques include Spline, Inverse Distance Weighting (IDW) and Kriging. A statistical assessment based on univariate statistics of the resulting continuous surfaces indicates that there is little difference in the predictive power of these techniques making hard the decision of selecting the best interpolation method. A Willmott statistical evaluation has been applied to minimize this uncertainty. Results show that IDW performs better for average and minimum temperature trends and Ordinary Kriging for maximum temperature trends. Results further indicate that temperature has an increasing trend all over Bangladesh noticably in the northern and coastal southern parts of the country. The temperature follows an overall increasing trend of 1.06°C per 100 years.

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Urban sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, adversely affects the provision of ecosystem services. The quantification of US is thus crucial for effective urban planning and environmental management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive US triggered by the doubling of total population over the past three decades. However, the extent and level of US has not yet been quantified and a prediction for future extent of US is lacking. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10 km suburban buffer of Chennai. The level of US was then quantified using Renyi’s entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services.

Large Scale Relationship between Aquatic Insect Traits and Climate

Climate is the predominant environmental driver of freshwater assemblage pattern on large spatial scales, and traits of freshwater organisms have shown considerable potential to identify impacts of climate change. Although several studies suggest traits that may indicate vulnerability to climate change, the empirical relationship between freshwater assemblage trait composition and climate has been rarely examined on large scales. We compared the responses of the assumed climate-associated traits from six grouping features to 35 bioclimatic indices (~18 km resolution) for five insect orders (Diptera, Ephemeroptera, Odonata, Plecoptera and Trichoptera), evaluated their potential for changing distribution pattern under future climate change and identified the most influential bioclimatic indices. The data comprised 782 species and 395 genera sampled in 4,752 stream sites during 2006 and 2007 in Germany (~357,000 km² spatial extent). We quantified the variability and spatial autocorrelation in the traits and orders that are associated with the combined and individual bioclimatic indices. Traits of temperature preference grouping feature that are the products of several other underlying climate-associated traits, and the insect order Ephemeroptera exhibited the strongest response to the bioclimatic indices as well as the highest potential for changing distribution pattern. Regarding individual traits, insects in general and ephemeropterans preferring very cold temperature showed the highest response, and the insects preferring cold and trichopterans preferring moderate temperature showed the highest potential for changing distribution. We showed that the seasonal radiation and moisture are the most influential bioclimatic aspects, and thus changes in these aspects may affect the most responsive traits and orders and drive a change in their spatial distribution pattern. Our findings support the development of trait-based metrics to predict and detect climate-related changes of freshwater assemblages.

An automated, objective and open source tool for stream threshold selection and upstream riparian corridor delineation

The extraction of stream networks from digital elevation models (DEMs) and delineation of upstream riparian corridors (URCs) for stream sampling points (SSPs) are frequently used techniques in freshwater and environmental research. Selection of an accumulation threshold (AT) for stream extraction and delineation of URCs are often done manually. Two algorithms are introduced in this paper that allow for automated AT selection and URC delineation. ATs are selected to yield the highest overlap of DEM-derived and traditionally mapped streams as well as to assure extraction of all mapped streams from DEMs. URCs are delineated after snapping SSPs to DEM-derived streams. The new tool showed similar or better performance than comparable algorithms and is freely available, interfacing the open source software packages R and GRASS GIS. It will improve the extraction of stream networks and the assessment of magnitude and scale of effects from riparian stressors (e.g. landuse) on freshwater ecosystems. Display Omitted Automated threshold selection for stream extraction.Goodness of fit of extracted networks with mapped stream networks.Automated delineation of upstream riparian corridors for stream sampling points.Algorithms developed by combining R and GRASS GIS.Computer code and data provided.

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

This study analyses the degree and margin of differences among the surfaces of annual total precipitation in wet days (PRCPTOT) and the yearly maximum value of the daily maximum temperature (TXx) of Bangladesh, produced by thin plate spline (TPS), inverse distance weighting (IDW), ordinary kriging (OK), and universal kriging (UK) methods of spatial interpolation. From the surface differences, the maximum and minimum differences are observed between the surfaces produced by TPS and IDW, and OK and UK, respectively. The residual plots from cross-validation depict that IDW and OK methods mostly under predict and TPS and UK methods mostly overpredict the observed climate indices’ values. Both the tendency of methods’ over and underprediction and the surface-differences decrease with the increase in the number of available spatial point observations. Finally, two performance measures—the index of agreement () and the coefficient of variation of prediction ()—imply that there is a little difference in the prediction ability of the four different methods. The performance of the spatial interpolation improves with the increase in the number of available spatial points, and eventually the predicted climate surfaces get similar. However, UK shows better interpolation performance in most of the years.

Temporal Patterns of the Two-Dimensional Spatial Trends in Summer Temperature and Monsoon Precipitation of Bangladesh

Two climate indices, TXx and PRCPTOT, representing the summer maximum temperature and annual total monsoon precipitation, respectively, in Bangladesh were computed. The temperature and precipitation measurements from 34 meteorological stations during the temporal extent of 1948–2007 were applied for indices’ computation under thorough quality control. The spatial trends of the indices were analyzed by applying two-dimensional least square approach along latitudes and longitudes of the observation points. The temporal patterns of the spatial trends were identified by temporally interpolating them applying thin plate smoothing spline method. The analyses of TXx identified regional scale spatial trends in the east-west and south-north directions, which were increasing between 1948 and 1980s. After the 1980s the spatial trends started decreasing, and after 2000 the spatial trend along the south-north changed its direction to the north-south and continued until present. The analyses of the PRCPTOT identified spatial trends in the west-east and north-south directions, which were decreasing between 1948 and 1980s and thereafter increasing until present. About half of the spatial trends were significant in F-statistics at or more than 90% confidence level. Thus, the obtained results indicated a significant climatic shift within the regional scale of the country during the study period.

A Remote Sensing Approach to Environmental Monitoring in a Reclaimed Mine Area

Mining for resources extraction may lead to geological and associated environmental changes due to ground movements, collision with mining cavities, and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e., subsidence, and degradation of vegetation productivity. Thus, monitoring short-term landscape dynamics in a reclaimed mine area may provide important information on the long-term geological and environmental impacts of mining activities. We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyze land-use and landcover dynamics, and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation productivity using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within four years, with 87.2% growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that the productivity of 66.5% of vegetation of the Kirchheller Heide was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide.