Urban growth monitoring and assessment are essential for the sustainable natural resources planning & optimum utilization and reducing the risk of problems arising from unplanned urban growth like pollution, urban heat island and ecological disturbances. Cellular Automata (CA) based modelling techniques have become popular in recent past for simulating the urban growth. Present study is aimed to evaluate the performance of the CA based SLEUTH model in simulating the urban growth of a complex and relatively more heterogeneous urban area, Ajmer city of Rajasthan (India) which is quite different as compared to areas where SLEUTH has been tested in developed countries. Seven multispectral satellite imageries spanning over 21 years have been processed and used for SLEUTH parameterisation. Results of urban growth predicted by SLEUTH has been compared with other methods of land use/land cover extraction. The study has been proved to be successful in giving significant insight into issues contributing uncertainties in forecasting of urban growth of heterogeneous urban areas

Simulation and prediction of land use land cover (LULC) has been presented using Hybrid CA-Markov model for the Mahi River basin. LULC information for the year 2000, 2010, and 2020 have been obtained from classification of Landsat TM, IRS -LISS III, and Sentinel 2 sensors, respectively. Fuzzy membership function and Analytical Hierarchical Process (AHP), a Multi-Criteria Evaluation (MCE) have been used for LULC change suitability map preparation. Population density, distance from road, settlement, streams, and reservoir/lakes, slope, and DEM are used as biophysical and socio-economic LULC change drivers. Model results have been validated for year 2020 and accuracy have been found as satisfactory 2020. Results revealed a significant change in forest, agriculture, barren and built-up LULC classes during different years. Agriculture and the built-up area may increase by 641 and 21 sq.km, respectively in 2030. The predicted LULC information provides necessary data to investigates the future hydrological and climatological scenario.

Objective. Sandstorms frequently cause adverse health effects especially in patients with asthma. The aim of our research was to explore the mechanism of sandstorm-induced asthmatic exacerbation by administering dust aerosol through an environmentally controlled exposure chamber. Methods. Four samples of soil (Ganganagar clay, Bikaner sand, Jaipur sand, and Ganganagar sand) were collected from three sandstorm-prone areas of Rajasthan, the desert state of India. Twenty patients with asthma, who had stable disease with a forced expiratory volume in first second (FEV₁) more than 70% of predicted, volunteered to participate in this randomized single-blind placebo-controlled crossover study. The four samples of dust and placebo were administered randomly on 5 study days. FEV₁ was measured for the next 60 minutes and the maximal decline in FEV₁ (ΔFEV₁) from baseline was measured. The samples of dust were also analyzed for particle size and adhesiveness. Results. The maximal decline in FEV₁ was observed 15 minutes post-exposure with all dust samples. Mean ΔFEV₁ was 0.69 ± 0.08 liters for Ganganagar clay, 0.52 ± 0.06 liters for Bikaner sand, 0.39 ± 0.07 liters for Jaipur sand, and 0.32 ± 0.04 liters for Ganganagar sand dust aerosol samples. Decline in FEV₁ correlated with volume of dust particles with size <10 μm (PM₁₀) and adhesiveness of the dust particles. Conclusion. Smaller-size sandstorm dust particles with higher adhesive properties have a greater potential of aggravating asthma.

The study demonstrates how to estimate evapotranspiration (ET) for the Western Rajasthan region of India utilizing remotely sensed images with the Surface Energy Balance Algorithm for Land (SEBAL). Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite inputs were used to compute seasonal and annual ET on the Google Earth Engine platform. The assessment utilizing the SEBAL algorithm, in combination with the Food and Agriculture Organization (FAO) Penman–Monteith (PM) and Hargreaves methods, demonstrates that SEBAL has adequate reliability for estimating ET for a spatially large extent in semi-arid regions when evaluated with the Hargreaves method. The values of R2, root-mean-square error (RMSE), and mean bias error (MBE) for FAO-PM were 0.63, 1.65 mm/day, and 1.28 mm/day, respectively. For the Hargreaves method, the values of R2, RMSE, and MBE were 0.96, 0.41 mm/day, and −0.31 mm/day, respectively. The study showed that the northern region witnessed the highest ET due to the availability of abundant surface water for irrigation. Overall, the results demonstrate the SEBAL model's effectiveness in estimating evapotranspiration. A downward trend in ET is observed in the region, mainly due to changing climatic conditions.

The development of the Indira Gandhi Nahar Pariyojana (IGNP) in western Rajasthan has significantly impacted land use and land cover (LULC) changes, which have hugely transformed the hydrologic cycle in recent decades. Hence, we investigate the surface evapotranspiration (ET) response to LULC changes in the IGNP for the period from 1990 to 2020. The Surface Energy Balance algorithm (Surface Energy Balance Algorithm for Land) was applied to the Landsat dataset along with local weather data obtained from the Indian Meteorological Department. A machine learning-based random forest classifier was applied to develop the LULC maps. The higher values of ET are mainly distributed in the northwest. Area covered by croplands has shown an increasing trend over the decades at a rate of 734.76 km2·a−1, urban settlements/ built-up areas increased at a rate of 14.67 km2·a−1, barren area decreased gradually at a rate of 751.67 km2·a−1, and area covered by the few water bodies showed minor fluctuations. Overall, the LULC changes have a smaller influence on ET rates but has a considerable impact on the total ET in the IGNP region due to an increase in the area covered by vegetation as a result of increased water availability.