Mapping of groundwater quality using Normalized Difference Dispersal Index of Dwarka sub-city at Delhi National Capital of India

Interpolation methods for spatial distribution of groundwater mapping electrical conductivity

This study was carried out to develop a conceptual framework for determining the best interpolation method which mainly is employed to calculate the variability maps of electrical conductivity (EC) in neighboring regions. The considered case study is parts of the Khorasan Razavi province, Iran (including five aquifers Kashmar, Fariman, Doruneh, Sarakhs and Joveyn). In the first step, the empirical variogram (semi-variogram) was computed for the study area. The methods of the variability of a variable with spatial or temporal distance were considered to measure the semi-variogram function. In the next step, the best variogram model (e.g. spherical, exponential or Gaussian) was considered in the Geographic Information System (GIS) environment and f for the Environmental Sciences (GS+) software. By plotting the semi-variogram in GS+ program based on different method as Global Polynomial Interpolation (GPI), Inverse distance weighing (IDW), Radial basis function (RBF), Kriging method, Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), the best variogram model fitted to spatial structure of the EC. Finally, by considering the acceptable range for different parameters which impact on EC and evaluating their impacts by scaling, the best interpolation method has been selected for that area for employing their neighborhood basin. Result indicated that the precipitation located within the range of 140 to 180 mm, RBI has the priority. This process is continued for all 14 parameters and eventually one method gets the most points.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

Evaluation of Surface Water Quality in the Betwa River Basin through the Water Quality Index Model and Multivariate Statistical Techniques

The Betwa River Basin (BRB), a sub-basin of the River Yamuna, is the oldest flowing water system in Central India. The water quality of the rivers are under stress, hence regular monitoring and appraisal is required to know the health of the rivers. Factor analysis and principal component analysis (FA/PCA) multivariate statistical techniques were used to extract three and four varimax factors that explained 96.408 and 100.00 percent of the total variance in water quality, respectively. Cluster analysis (CA) categorizes observed items into distinct quality categories based on correlations between stations and years. Point industrial/sewage effluents, diffuse pollution as runoff from arable land, erosion, and natural source pollution contribute to the pollution of the BRB. As a result, water quality is threatened or impaired, and conditions often departed from natural or desirable levels at Rajghat, Garrauli, Mohana, and Shahijina stations. According to the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), the surface water quality at the Rajghat and Mohana stations corresponds to fair ecological status. However, the surface water quality of the Garrauli and Shahijina stations has a marginal water quality as per CCME-WQI. From 1985 to 2018, the Shahijina had the most considerable load of nutrients and organic matter, as determined by the CCME-WQI and by comparing the water quality data. A thorough examination had revealed a fluctuating trend in the BRB pollution, particularly at all stations. Results indicate that between 1985 and 2018, the only defense mechanism of the river was the auto purification mechanism, which is strongly influenced by the drought, point pollution source, and extreme meteorological events that probably cause these fluctuations.