Stubble burning during October and November, results in the extensive formation of smoke cloud over the Punjab region, and maybe one of the main reasons behind the increase in air pollution levels in these areas. The manual detection and estimation are tedious, lengthy and unpractical, so several researchers have been using remote sensing and GIS technique to estimate stubble burn areas and forest fires. Thus, in the present study, an attempt has been made to detect and estimate the stubble burn area. Landsat 8 OLI images are used to detect the stubble burn area for the year 2014-18 for Patiala and Ludhiana, which are major rice producing districts of Punjab. Normalize Burn Ratio (NBR) index have been used to determine the burned area in an image using a statistical threshold technique (2σ approach). The results have been validated using available as well as collected Ground Control Points (GCPs) and accuracy assessment has been conducted by generating an error matrix. It has been estimated that the stubble burn area was reduced by 32% and 40% during the study period for Patiala and Ludhiana regions, respectively. The monthly variation for various pollutants (RSPM, NO_x, and SO₂) during the study period has also been studied and analyzed. The distinct increase in pollutant levels has been observed during each stubble burning period. The results also indicate that the amount of emitted RSPM and NO_x was higher than the emitted SO₂ during stubble burning. The wind rose diagrams have also been plotted.

This study utilizes surface displacement data from Persistent Scatterer SAR Interferometry (PSInSAR) of Sentinel-1 satellite and groundwater storage change data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to understand land subsidence in the Chandigarh tri-city region. The satellite datasets are used along with the groundwater level data obtained from wells over the study area. Since the GRACE data are available at a much coarser spatial resolution of 1o by 1o, challenges remain in correlating the dataset with PSInSAR displacement that has been multi-looked at 14 m by 14 m resolution. Therefore, multiple sources of data (i.e., the monthly average of GRACE data, groundwater storage change and monthly average PSInSAR displacement per pixel, and interpolated groundwater level data from wells for 2017 to 2022) have been deployed into a deep learning multi-layer perceptron (DLMLP) model to estimate the groundwater storage change at the urban level. This has an indirect downscaling method that is carried out successfully using the DLMLP model for the estimation of groundwater storage changes at the urban level, which is usually complicated by applying direct downscaling methods on the GRACE data. Thus, the DLMLP model developed here is a distinctive approach considered for estimating the changes in groundwater storage using PSInSAR displacement, groundwater data from wells, and GRACE data. The DLMLP model gives an R²-statistics value of 0.91 and 0.89 in the training and testing phases, respectively, and has a mean absolute error (MAE) of 1.23 and root mean square error (RMSE) of 0.87.

Groundwater is a vital resource of potable water in India as almost all industries require water due to one or other reasons. It may get polluted by industrial waste, leachate from dumping yards, rampage, and uncontrolled use of pesticides. The physicochemical properties of water give a general indication of the interference capability of water with the normal biological process of the environment. Most heavy metals are present in trace amounts in the groundwater and useful for the human body. However, if they exceed the permissible limits these may cause health hazards. In this study, an attempt has been made to assess, analyze, and mapping groundwater quality using the Geographical information system (GIS). In this study, five heavy metals (aluminium, arsenic, cadmium, iron, selenium) and six physiochemical parameters (pH, total hardness, alkalinity, chloride, fluoride) have been used to achieve the objectives. Gurdaspur (Punjab, India) was selected for the study as it ranks fourth in the cancer cases after Amritsar, Ludhiana, and Bathinda as per the government reports. Field visits were made to collect samples and preservation techniques were followed according to standard methods (APHA 2005). Single-digit indices like the water quality index (WQI), heavy metal pollution index (HPI), metal index (MI) were used to classify the quality of water. Volumetric analysis and ion chromatography tests were carried out on the grab samples of 25 locations, which were collected in Dec’2019. Arsenic was found to exceed the permissible value in nearly 80% of samples. It has been observed that MI values exceeded the critical value of 1, indicating the pollution caused by the individual metal was more as compared to the pollution caused by all metals together. IDW interpolation technique was applied to all indices using GIS software to determine spatial distribution. Overall, WQI was found to be satisfactory, with only 20% of the sampling stations exceeding the acceptance value. The average value of the heavy metal pollution index (HPI) was 80% of the sampling stations. Further, an attempt has been made to determine the cancer risk of the inhabitants in the study area. The health risk index (HRI) was determined by chronic daily intake and hazard quotient as per USEPA. The hazard quotient (HQ) value of heavy metals was found to be greater than unity in all the selected areas, indicating that heavy metals pose a health threat in this region. It has been observed that the hazard quotient value is higher for children than adults, implying more vulnerability of children to a potential health hazard. Actual total cancer risk was found to be 33.65×10⁻⁴, which means ∼34 persons in 10,000 people have the possibility of cancer. A hypothesis was made which reduced the theoretical risk to 31.65×10⁻⁴ which was found to be comparable to the actual cancer risk. It has been observed that the cancer risk for the children and adults were in the “high risk” category. The higher values of arsenic can be attributed to higher cancer risk and health quotient value. Both children and adults were found to be risk-prone to both carcinogen and noncarcinogen health effects, with children on the higher side.