Biophysical and socioeconomic characterization of a water-stressed area and simulating agri-production estimates and land use planning under normal and extreme climatic events: a case study

This study aims in linking the biophysical and socioeconomic data base layers with the technical coefficients or simulation models for agri-production estimates and land use planning under normal and extreme climatic events, and exploring the resource and inputs management options in village Shikohpur, Gurgaon district located in the northwest part of India. The socioeconomic profile of Shikohpur is highly skewed with mostly small and marginal farmers. Though the areas under wheat in Shikohpur are increasing, the productivity is declining or remaining stagnant over the years. Most of the area during kharif season (June-September) remains fallow. Pearl millet based cropping systems (pearl millet-mustard and pearl millet-wheat) are predominant. Soils are mostly loamy sand to sandy loam with average of 70-80% sand content. Organic C content in soil is less than 0.3%, due to high prevailing temperature with little rainfall and also intensive agriculture followed in this region. Though the annual average seasonal rainfall in Gurgaon did not have much variation over the years, occurrence of extreme climate events has increased in the last two decades. The crop intensity is low and the water table is declining. Water and nitrogen production functions were developed for the important crops of the region, for their subsequent use in scheduling of the inputs. InfoCrop, WTGROWS and technical coefficients were used for crop planning and resource management under climate change and its variability, extreme events, limited resource availability and crop intensification. These will help in disseminating necessary agro-advisories to the farmers so that they will be able to manipulate the inputs and agronomic management practices for sustained agricultural production under normal as well as extreme climatic conditions.     

Impact of water quality on bacterioplankton assemblage along Cértima River Basin (central western Portugal) assessed by PCR–DGGE and multivariate analysis

The information on bacterial community composition (BCC) in Portuguese water bodies is very scarce. Cértima River (central western Portugal) is known to have high levels of pollution, namely organic. In the present work, the BCC from a set of 16 water samples collected from Cértima River Basin and its main tributaries was characterized using 16S rDNA–denaturing gradient gel electrophoresis, a culture-independent molecular approach. Molecular data were related to environmental parameters through multivariate analysis to investigate potential impact of water pollution along the river. Principal component analysis using environmental data showed a water quality gradient from more pristine waters (at the mountain tributaries) to waters with increasingly eutrophic potential (such as Fermentelos Lake). This gradient was mainly defined by factors such as organic and inorganic nutrient sources, electrical conductivity, hydrogen carbonate concentration, and pH. Molecular results showed variations in BCC along Cértima River Basin but in the main river section, a Bacteroidetes phylotype (Flavobacterium sp.) proved to be dominant throughout the river course. Multivariate analysis suggests that spatial variation of BCC along the Cértima River Basin depended mainly on parameters such as Chl a, total suspended solid (TSS), total organic carbon, electrical conductivity, and HCO _boxclose^-_{3}^{-} levels. Bacteroidetes phylotypes were all related to higher electrical conductivity and HCO₃^-_{3}^{-} levels although some of these were also correlated with high SO₄^2-_{4}^{2-} and others with high soluble reactive phosphorus, nitrate, TN, and Kjeld-N levels. The Gammaproteobacteria occurrence was correlated with high SO₄^2-_{4}^{2-} levels. One of the Betaproteobacteria phylotypes showed to correlate with low redox potential (E_h) and high temperature, pH, TSS, and Chl a levels while another one showed a negative correlation with Chl a values.     

Mixed response in bacterial and biochemical variables to simulated sand mining in placer-rich beach sediments,Ratnagiri, West coast of India

We investigated the influence on bacterial community and biochemical variables through mechanical disturbance of sediment-akin to small-scale mining in Kalbadevi beach, Ratnagiri, a placer-rich beach ecosystem which is a potential mining site. Changes were investigated by comparing three periods, namely phase I before disturbance, phase II just after disturbance, and phase III 24 h after disturbance as the bacterial generation time is ≤7 h. Cores from dune, berm, high-, mid-, and low-tide were examined for changes in distribution of total bacterial abundance, total direct viability (counts under aerobic and anaerobic conditions), culturability and biochemical parameters up to 40 cm depth. Results showed that bacterial abundance decreased by an order from 10⁶ cells g^− 1 sediment, while, viability reduced marginally. Culturability on different-strength nutrient broth increased by 155% during phase II. Changes in sedimentary proteins, carbohydrates, and lipids were marked at berm and dune and masked at other levels by tidal influence. Sedimentary ATP reduced drastically. During phase III, Pearson’s correlation between these variables evolved from non-significant to significant level. Thus, simulated disturbance had a mixed effect on bacterial and biochemical variables of the sediments. It had a negative impact on bacterial abundance, viability and ATP but positive impact on culturability. Viability, culturability, and ATP could act as important indicators reflecting the disturbance in the system at short time intervals. Culturability, which improved by an order, could perhaps be a fraction that contributes to restoration of the system at bacterial level. This baseline information about the potential mining site could help in developing rational approach towards sustainable harnessing of resources with minimum damage to the ecosystem.     

Dibenzazepin hydrochloride as a new spectrophotometric reagent for determination of hydrogen peroxide in plant extracts

A rapid, simple, accurate, and sensitive visible spectrophotometric method for the determination of trace amounts of hydrogen peroxide in acidic buffer medium is reported. The proposed method is based on the oxidative coupling of Ampyrone with dibenzazepin hydrochloride by hydrogen peroxide in the buffer medium of pH?4.0 which is catalyzed by ferrous iron. The blue-colored product formed with maximum absorption at 620?nm was found to be stable for 2?h. Beer's law is obeyed for hydrogen peroxide concentration in the range of 0.03-0.42?μg?ml(-1). The optimum reaction conditions and other important optical parameters are reported. The molar absorptive and Sandell's sensitivity are found to be 5.89?×?10(4)?mol(-1)?cm(-1) and 0.57?g/cm(2), respectively. The interference due to diverse ions and complexing agents was studied. The method is successfully applied to the determination of hydrogen peroxide in green plants satisfactorily.     

Particulate matter concentration in ambient air and its effects on lung functions among residents in the National Capital Region, India

The World Health Organization has estimated that air pollution is responsible for 1.4 % of all deaths and 0.8 % of disability-adjusted life years. NOIDA, located at the National Capital Region, India, was declared as one of the critically air-polluted areas by the Central Pollution Control Board of the Government of India. Studies on the relationship of reduction in lung functions of residents living in areas with higher concentrations of particulate matter (PM) in ambient air were inconclusive since the subjects of most of the studies are hospital admission cases. Very few studies, including one from India, have shown the relationship of PM concentration and its effects of lung functions in the same location. Hence, a cross-sectional study was undertaken to study the effect of particulate matter concentration in ambient air on the lung functions of residents living in a critically air-polluted area in India. PM concentrations in ambient air (PM_1, PM_2.5) were monitored at residential locations and identified locations with higher (NOIDA) and lower concentrations (Gurgaon). Lung function tests (FEV₁, PEFR) were conducted using a spirometer in 757 residents. Both air monitoring and lung function tests were conducted on the same day. Significant negative linear relationship exists between higher concentrations of PM₁ with reduced FEV₁ and increased concentrations of PM_2.5 with reduced PEFR and FEV₁. The study shows that reductions in lung functions (PEFR and FEV₁) can be attributed to higher particulate matter concentrations in ambient air. Decline in airflow obstruction in subjects exposed to high PM concentrations can be attributed to the fibrogenic response and associated airway wall remodeling. The study suggests the intervention of policy makers and stake holders to take necessary steps to reduce the emissions of PM concentrations, especially PM_1, PM_2.5, which can lead to serious respiratory health concerns in residents.     

Multivariate analysis of potentially toxic metals in sediments of a tropical coastal lagoon

Surface sediments collected from the Lagos Lagoon, Nigeria, and three adjoining rivers were analysed for their physicochemical properties and pseudo-total concentration of the potentially toxic metals (PTM) Cd, Cr, Cu, Pb and Zn. The concentration of the PTM varied seasonally and spatially. Odo-Iyaalaro was observed to be the most polluted river, with highest concentrations of 42.1 mg kg^?1, 102 mg kg^?1, 185 mg kg^?1, 154 mg kg^?1 and 1040 mg kg^?1 of Cd, Cr, Cu, Pb and Zn, respectively, while Ibeshe River was the least contaminated, apart from a site affected by Cu from the textile industry. Some of the sediments were found to be above the consensus-based probable effect concentrations and Dutch sediment guideline for metals. Overall metal concentrations were similar to those reported for other tropical lagoon and estuarine systems affected by anthropogenic inputs as a result of rapid urbanisation. Due to the large number of samples, principal component analysis was used to examine relationships within the data set. Generally, sediments collected during the dry season were observed to have higher concentration of PTM than those collected during the rainy season. This means that PTM could accumulate over a prolonged period and then be released relatively rapidly, on an annual basis, into tropical lagoon systems.     

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC_e 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.     

Analysis of long-term water quality for effective river health monitoring in peri-urban landscapes—a case study of the Hawkesbury–Nepean river system in NSW, Australia

The Hawkesbury–Nepean River (HNR) system in South-Eastern Australia is the main source of water supply for the Sydney Metropolitan area and is one of the more complex river systems due to the influence of urbanisation and other activities in the peri-urban landscape through which it flows. The long-term monitoring of river water quality is likely to suffer from data gaps due to funding cuts, changes in priority and related reasons. Nevertheless, we need to assess river health based on the available information. In this study, we demonstrated how the Factor Analysis (FA), Hierarchical Agglomerative Cluster Analysis (HACA) and Trend Analysis (TA) can be applied to evaluate long-term historic data sets. Six water quality parameters, viz., temperature, chlorophyll-a, dissolved oxygen, oxides of nitrogen, suspended solids and reactive silicates, measured at weekly intervals between 1985 and 2008 at 12 monitoring stations located along the 300 km length of the HNR system were evaluated to understand the human and natural influences on the river system in a peri-urban landscape. The application of FA extracted three latent factors which explained more than 70 % of the total variance of the data and related to the ‘bio-geographical’, ‘natural’ and ‘nutrient pollutant’ dimensions of the HNR system. The bio-geographical and nutrient pollution factors more likely related to the direct influence of changes and activities of peri-urban natures and accounted for approximately 50 % of variability in water quality. The application of HACA indicated two major clusters representing clean and polluted zones of the river. On the spatial scale, one cluster was represented by the upper and lower sections of the river (clean zone) and accounted for approximately 158 km of the river. The other cluster was represented by the middle section (polluted zone) with a length of approximately 98 km. Trend Analysis indicated how the point sources influence river water quality on spatio-temporal scales, taking into account the various effects of nutrient and other pollutant loads from sewerage effluents, agriculture and other point and non-point sources along the river and major tributaries of the HNR. Over the past 26 years, water temperature has significantly increased while suspended solids have significantly decreased (p?<?0.05). The analysis of water quality data through FA, HACA and TA helped to characterise the key sections and cluster the key water quality variables of the HNR system. The insights gained from this study have the potential to improve the effectiveness of river health-monitoring programs in terms of cost, time and effort, particularly in a peri-urban context.     

Prediction of the solubility of zinc, copper, nickel, cadmium, and lead in metal-contaminated soils

Risk assessment of metal-contaminated soil depends on how precisely one can predict the solubility of metals in soils. Responses of plants and soil organisms to metal toxicity are explained by the variation in free metal ion activity in soil pore water. This study was undertaken to predict the free ion activity of Zn, Cu, Ni, Cd, and Pb in metal-contaminated soil as a function of pH, soil organic carbon, and extractable metal content. For this purpose, 21 surface soil samples (0–15 cm) were collected from agricultural lands of various locations receiving sewage sludge and industrial effluents for a long period. One soil sample was also collected from agricultural land which has been under intensive cropping and receiving irrigation through tube well water. Soil samples were varied widely in respect of physicochemical properties including metal content. Total Zn, Cu, Ni, Cd, and Pb in experimental soils were 2,015?±?3,373, 236?±?286, 103?±?192, 29.8?±?6.04, and 141?±?270 mg kg^?1, respectively. Free metal ion activity, viz., pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺, as estimated by the Baker soil test was 9.37?±?1.89, 13.1?±?1.96, 12.8?±?1.89, 11.9?±?2.00, and 11.6?±?1.52, respectively. Free metal ion activity was predicted by pH-dependent Freundlich equation (solubility model) as a function of pH, organic carbon, and extractable metal. Results indicate that solubility model as a function of pH, Walkley–Black carbon (WBC), and ethylenediaminetetraacetic acid (EDTA)-extractable metals could explain the variation in pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺ to the extent of 59, 56, 46, 52, and 51 %, respectively. Predictability of the solubility model based on pH, KMnO₄-oxidizable carbon, and diethylenetriaminepentaacetic acid-extractable or CaCl₂-extractable metal was inferior compared to that based on EDTA-extractable metals and WBC.     

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EF_G) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EF_G techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EF_G technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.