Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm.

Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and 8 satellite images to prepare LULC maps from 2000 to 2020 in every 5 years. The atmospherically corrected images were pre-processed for removal of cloud cover and then classified into six classes: waterbodies, farmland, settlement, open forest, dense forest, and bare land. We applied four machine learning (ML) algorithms for classification, namely, random forest (RF), classification and regression tree (CART), minimum distance (MD), and support vector machine (SVM) for the images from 2000 to 2020. With 80% training and 20% testing data, we found that the RF classifier works best with the most accuracy than other classifiers. The average overall accuracy (OA) and Kappa coefficient (KC) from 2000 to 2020 were 84.00% and 0.79 when the RF classifier was used. When using SVM, CART, and MD, the average OA and KC were 78.06%, 0.73; 78.60%, 0.72; and 73.32%, 0.65, respectively. We utilised three methods of topographic correction, namely, C-correction, SCS (sun canopy sensor) correction, and SCS + C correction to reduce the misclassification due to shadow effects. SCS + C correction worked best for this region; hence, we prepared LULC maps on SCS + C corrected satellite image. Hence, we have used RF classifier for LULC preparation demi-decadal from 2000 to 2020. The OA for 2000, 2005, 2010, 2015, and 2020 was found to be 84%, 81%, 81%, 85%, and 89%, respectively, using RF. The dense forest decreased from 2000 to 2020 with an increase in open forest, settlement, and agriculture; nevertheless, when Farmland was low, there was an increase in the barren land. The results were significantly improved with the topographic correction, and misclassification was quite less.

Implications of Russia-Ukraine war on land surface temperature and air quality: long-term and short-term analysis.

The world is currently witnessing the military operations of Russia invading Ukraine, leading to missile bombing and shelling on various parts. Although the economic ill effects are more conspicuous and much talked about, the environmental impacts are grimmer and more devastating but ironically are less in the news. Hence, in this work, we focused on the environmental impact of the Russia-Ukraine war by quantifying the long-term (2001 to 2023) and short-term temperature changes using land surface temperature (LST) and air temperature (AT) as proxies and monitoring changes in air quality, mainly methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2), between 2021 and 2022. We used NASA MODIS FIRMS fire points from 24th February 2022 to 08th September 2023 to prepare the heat map for identifying the regions heavily devastated by bombing. Thus, parts of Kiev, Lviv, Luhansk, Odesa, Donetsk, Kherson, etc., in Ukraine were chosen for assessing the LST, AT variations during the peak season of war along with analysis of long-term and short-term changes. We used MODIS Terra LST and Emissivity, ERA 5 AT, CH4, CO2 from AIRS and CO from Sentinel 5P. The results of the LST showed an average increase of around 2.32 °C (2022-2023), 3.44 °C (2021 and 2022) in parts of Ukraine and an increase of about 2 °C from COVID time, whilst a decrease of about 1 °C during COVID. This increase in LST will cause enhanced warming, thus changing the regional climate in a shorter time frame. A consistent upward trend in CH4, CO and CO2 is seen from 2019 to 2023. These heat waves and pollution will grip Ukraine and cause menace due to the cumulative effect of heat waves, changing climate and the aftermaths of war. This would be catastrophic as it might lead to a widespread impact on human health, agricultural yield and infrastructure, to name a few.

Spatio-temporal evolution of groundwater quality and its health risk assessment in Punjab (India) during 2000-2020.

The state known as the bread basket of India has now been defamed as the cancer capital of the country. The toxicity of groundwater associated with the declining water level is reported in recent years. However, an extensive temporal and spatial analysis is required to identify hotspots. In this study, spatial tools are utilized to understand the evolution of groundwater in Punjab (> 315 sites) for the last two decades (2000-2020) for drinking purposes using the water quality index (WQI). The data for pH, electric conductivity (EC), bicarbonate (HCO3¯), chloride (Cl¯), sulfate (SO42¯), nitrate (NO3¯), fluoride (F¯), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and potassium (K+) collected from the Central Groundwater Board (CGWB) were analyzed. The results show that the average cation abundance is in declining order of Na > Mg > Ca > K, and anion abundance is in order of HCO3¯ > SO42¯ > Cl¯ > NO3 > F. The ions are compared with water quality standards defined by BIS and WHO. The study shows that in the year 2000, 69.52% of locations are above the acceptable limit for EC, 68.89% for Mg2, 84.76% for Na+, 51.75% for HCO3¯, 38.41% for NO3¯, and 17.20% for F¯. While in the year 2020, 48.89% exceed the acceptable limit for EC, 57.78% for Mg2+, 68.25% for Na+, 34.92% for HCO3¯, 27.30% for NO3¯, and 8.88% for F¯. WQI shows that in the year 2000, 13.01% of sampling locations are categorized as very poor and 20% as unsuitable for drinking. Meanwhile, in 2020, 6.35% of locations are categorized as very poor and 12.38% as unsuitable for drinking in the study area. In addition to the effect on plant growth, consumption of contaminated water can adversely affect human health. The health hazards for F¯ (HQF) and NO3¯ (HQN) and their total health index (THI) are also evaluated that depicts 244 groundwater sampling sites in the year 2000, and 152 sampling sites in the year 2020 show high non-carcinogenic effects on adults, children, and infants. Southwestern Punjab is found to be the worst affected, while north-eastern regions drained by the Himalayan rivers show better quality water. Shifting in agricultural practices in the last two decades and declining water levels due to excess pumping of water from deeper water tables deteriorated the quality of water in the Southern region as observed from the geospatial analysis.

Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach.

The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F-score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F-score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides.

Data on Arsenic(III) removal using zeolite-reduced graphene oxide composite.

This data article proposes a mechanisms of Arsenic(III) removal from water using zeolite-reduced graphene oxide (ZrGO) composite. Here, the adsorption kinetic and adsorption isotherm analysis for the data obtained by removal of Arsenic(III) using ZrGO is presented. The kinetic model fits the pseudo second order kinetics and indicates the adsorption mechanism to be chemisorption. Redlich Peterson isotherm model best describes the adsorption isotherm. The data are related to the research article "Synthesis of Fly ash based zeolite-reduced graphene oxide composite and its evaluation as an adsorbent for arsenic removal" (Soni and Shukla, 2019).

Synthesis of fly ash based zeolite-reduced graphene oxide composite and its evaluation as an adsorbent for arsenic removal.

A zeolite-reduced graphene oxide (ZrGO) based composite was synthesized to remove arsenic from water. To make a low-cost adsorbent, zeolite was synthesized using an inexpensive waste material; fly ash, which was further used to produce the ZrGO composite. Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and Raman spectra were used to characterize the morphology and surface composition of the synthesized materials. Synthesized materials: zeolite, rGO and ZrGO were evaluated as an adsorbent to remove arsenic from water. The results indicated that all three were able to adsorb arsenic from water but the removal efficiency of ZrGO was the best as it was able to bring down the arsenic concentration within the WHO permissible limits. The maximum adsorption capacity for 100 µg/L of initial arsenic concentration was found to be 49.23 µg/g. Results indicate that pseudo second order kinetics describes the arsenic adsorption on ZrGO. Adsorption isotherm study for ZrGO shows best fit for Redlich-Peterson model of adsorption.

Assessment of land use/land cover dynamics of Tso Moriri Lake, a Ramsar site in India.

Wetlands accounts for 6% area of the Earth's land cover and nearly 17% of the Hindu Kush Himalayan region. They are of utmost importance to climate dynamics and are critical links between terrestrial and aquatic ecosystems. Despite the need of high attention towards conserving and managing wetland resources, mapping them is a least practiced activity. This study shows the temporal change in land use and land cover pattern of Tso Moriri Lake, the highest altitude lake in India and designated as Ramsar site in year 2002, using multi-sensor and multi-date imagery. Due to change in hydro-meteorological conditions of the region, this lake area has been reduced. Since the lake recharge is dependent on snowmelt, hence change in climatic conditions (less snowfall in winters), to a certain extent, is also responsible for the decrease in water level and water spread of the lake. The result shows that the lake area has reduced approximately 2 km2 in the last 15 years, and also, agriculture, grasslands, and vegetation cover have increased to a significant extent. Agricultural land and grasslands have doubled while the vegetation cover has increased more than six times, showing the coupled effect of climate change and anthropogenic activities. Trend of temperature and precipitation corroborates the effects of climate change in this region.