The Energy and Resources Institute (TERI) is a research institute in New Delhi that specializes in the fields of energy, environment and sustainable development. Established in 1974, it was formerly known as the Tata Energy Research Institute. As the scope of its activities widened, it was renamed The Energy and Resources Institute in 2003.
Microbial diversity of bacterial species in home-made curd from different states of India was analyzed using genotyping-by-sequencing. The most prevalent species were Lactobacillus delbrueckii subsp. bulgaricus, Lactobacillus helveticus, Streptococcus thermophilus and Lactobacillus kefiranofaciens. Pathogenic species such as Salmonella enterica serovar Typhimurium and Shigella dysenteriae were found in over 60% of samples. High intraspecific variation was observed in Acinetobacter baumannii, Bacillus cereus, Enterococcus faecium, Escherichia coli, Lactococcus lactis subsp. cremoris, Lactococcus piscium, Salmonella enterica serovar Typhimurium and Shigella dysenteriae. Intra-specific genetic relatedness in Lactobacillus delbrueckii subsp. bulgaricus in curd samples largely correlated with their respective sites of collection. ### Competing Interest Statement The authors have declared no competing interest.
The study investigates climate–agriculture interactions by integrating machine learning models with participatory household surveys, a novel dual approach in the context of climate change. We combined wheat crop statistics, vegetation indices, and climatic parameters from 2001 to 2021 with survey data from 292 farming households in Aligarh district, Uttar Pradesh, India. The temporal analysis revealed increasing trends in wheat yields and vegetation indices, with December–January being the months when EVI and NDVI showed strong positive correlations with wheat yields. Furthermore, vegetation indices were negatively associated with the diurnal temperature range but positively correlated with precipitation. March emerged as a crucial month, as rising temperatures, vapor pressure, and potential evapotranspiration had an adverse effect on vegetation indices. Among the models tested, random forest (RF) outperformed support vector machine (SVM) and multiple linear regression (MLR), demonstrating its robustness for predicting nonlinear crop–climate relationships. Household surveys revealed a high awareness of climate change, but significant barriers to adaptation were also reported, including a lack of capital and small landholdings. It indicated a need to connect awareness with adoption, which is crucial for successful adaptation interventions. By combining top-down (machine learning) and bottom-up (participatory) approaches, the research contributes a novel framework for climate-resilient policy planning. To the best of our knowledge, this is one of the first studies in India to integrate quantitative modeling with qualitative social insights at the localized district scale for a region underrepresented in climate–agriculture studies. The findings offer actionable insights for informed agrarian policy-making and lay a foundation for future integrative research on climate adaptation in rural areas.
Rapid urbanization and industrial growth in developing countries have intensified environmental degradation, prominently exemplified by increasing concentrations of ambient particulate matter (PM2.5), significantly affecting public health and the economy. This study focuses on predicting daily PM2.5 levels in Delhi, India, a city characterized by complex air quality dynamics due to diverse anthropogenic and meteorological influences. To capture these complexities, we leveraged data from 39 monitoring stations (2019 – 2023) and developed a multi-model framework, employing statistical approaches (SARIMAX), machine learning algorithms (Random Forest, Support Vector Machines), and deep learning models (Artificial Neural Networks, Long Short-Term Memory Networks). The framework uniquely combines station-specific hyperparameter optimization, comprehensive exogenous variables (co-pollutants: PM₁₀, NO₂, SO₂, O₃, CO; and meteorological parameters), and Fourier-transformed functions which explicitly capture the multi-scale seasonal variations. Modeling results reveal ANN as the top performer (testing R2 = 0.81–0.98, RMSE = 10.75–31.96 µg/m3, MAE = 7.71–19.42 µg/m3), followed closely by Bi-LSTM (R2 = 0.79–0.96, RMSE = 13.99–32.45 µg/m3, MAE = 8.05–18.92 µg/m3). RF demonstrated robust intermediate accuracy (R2 = 0.79–0.95, RMSE = 15.33–34.87 µg/m3, MAE = 9.84–24.13 µg/m3), outperforming SARIMAX and SVM models. Fourier terms enhanced prediction stability by capturing seasonal dynamics, while station-specific tuning improved localized accuracy. These robust predictions are crucial for timely public health advisories and evidence-based policymaking, ultimately aiming to mitigate health risks and facilitate sustainable urban environmental management in one of the world’s most polluted cities.
The outbreak of the novel Coronavirus disease (COVID-19) in December 2019 in Wuhan, China, rapidly spread across the globe, impacting over 210 countries, including India. The nationwide lockdown in 2020 significantly improved air quality due to the suspension of transport, industrial, and construction activities. This study evaluates the variations in PM₂.₅ and PM₁₀ concentrations across multiple monitoring stations in Delhi, India, from 2019 to 2021, along with the influence of meteorological parameters and associated health risks. The mean concentrations of PM₂.₅ and PM₁₀ were 109.54 µg/m³ and 214.04 µg/m³, respectively, for the study period. Year-wise analysis revealed average PM₂.₅ levels of 113.33 µg/m³ (2019), 108.92 µg/m³ (2020), and 106.92 µg/m³ (2021), while PM₁₀ levels were 221.34 µg/m³, 203.02 µg/m³, and 220.04 µg/m³, respectively. A substantial reduction in PM₂.₅ was observed during the lockdown, attributed to restricted anthropogenic activities. Strong positive correlations were found for PM₂.₅ and PM₁₀ among Alipur, ITO, Okhla, Narela, and Wazirpur stations. Meteorological analysis indicated a negative correlation between PM concentrations and temperature, wind speed, and solar radiation, whereas moisture and wind direction showed a positive relationship. The health risk assessment, based on Hazard Quotient (HQ) values, indicated that children and infants were more vulnerable to adverse health effects than adults, particularly in the pre- and post-pandemic periods. These findings emphasize the role of emission control strategies in improving air quality and mitigating health risks.
