Can Landuse Landcover changes influence the success of India's national clean air plans ?

Atmospheric Environment X, Journal Year: 2024, Volume and Issue: 22, P. 100251 - 100251

Published: March 23, 2024

India implemented a range of multifarious strategies to address the issue substandard air quality. One such flagship scheme government is National Clean Air Programme (NCAP), which recommends sector specific reduction in emissions and increase forest cover etc. To reduce particulate matter concentrations by 40% 2026 compared 2019. The present study aims gauge impact Land Use Cover (LULC) changes alone on success NCAP, using weather research forecasting model with chemistry (WRF-Chem) integrated geographical information system remote sensing software Terrset. findings elucidate that, year 2026, Ventilation Coefficient (VC) India's eastern, central, northern, north-eastern regions anticipated register decline ranging from 18% 50% baseline Conversely, an 17% expected southern region. alterations Fallow Land, Barren sparsely vegetated land, Urban Built-up Tundra, contribute these shifts, displaying varying percentage across distinct zones. Simulations indicate that LULC are impeding planned PM2.5 levels. Projections suggest levels as high 13% regions, accompanied decrease 33% Southern zone country. Significantly, non-attainment cities Himachal Pradesh Maharashtra witness substantial rise PM2.5-induced premature mortality, Pune city projected experience over 24,525 additional deaths 2026. A comparable examination conducted for 2022, utilizing actual data, suggests if NCAP fails effectively implement changes, it may this trade-off. Addressing concern, employed WRF-Chem simulate 60 combinations, proposing enhancements conducive improving VC. results underscore critical importance preserving at least 36% category mixed encompassing plantations, orchards, areas under shifting agriculture. Additionally, barren land fallow emerges pivotal enhancing ventilation coefficient. accentuates necessity refraining further expansion densely populated counter VC trends. This provides valuable insights, highlighting need prioritize management combat alarming pollution.

Language: Английский

---

Identifying influential climatic factors for urban risk studies in rapidly urbanizing Region

Computational Urban Science, Journal Year: 2024, Volume and Issue: 4(1)

Published: April 11, 2024

Abstract Severe weather events, such as heat waves, floods, pollution, and health threats, are becoming more common in metropolitan places across the world. Overcrowding, poor infrastructure, fast, unsustainable urbanization some of problems that India faces, country is also susceptible to natural disasters. This research analyzes climatic variables affecting urban hazards Bangalore (also known Bengaluru) via a thorough review. Heat islands, drought were identified 156 qualifying publications using Preferred Reporting Items for Systematic Reviews Meta-Analysis (PRISMA) method. Contributing considered. City development key changing climate increasing dangers. While long-term variable distribution uneven, warming evident. The report promotes strong planning techniques, comprehensive policies, green areas, sustainable beyond short-term response programs boost resilience. study shows how climate, land use, dangers interconnected. Future studies may benefit by categorizing risk identifying factors.

Language: Английский

---

Analyzing methane emissions in five Indian cities using TROPOMI data from sentinel-5 precursor satellite

Urban Climate, Journal Year: 2024, Volume and Issue: 58, P. 102174 - 102174

Published: Oct. 19, 2024

Language: Английский

---

Machine learning and deep learning approaches for PM2.5 prediction: a study on urban air quality in Jaipur, India

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 22, 2024

Language: Английский

---

Assessment of meteorological parameters in predicting seasonal temperature of Dhaka city using ANN

Heliyon, Journal Year: 2024, Volume and Issue: 10(14), P. e34253 - e34253

Published: July 1, 2024

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction temperature Dhaka city. Prior knowledge is essential, especially in tropical regions like Dhaka, as it aids forecasting heatwaves and implementing effective preparedness schemes. While various models have employed hot weather across world, research specially focused on Bangladesh limited. Additionally, application needs be curated suit particular features any region. Therefore, study approaches ANN method exploring underlying role related variables. Using daily data months February July collected from National Center Environmental Prediction/National Atmospheric Research (NCEP/NCAR) reanalysis (0.25° × 0.25° global grid) years 2011–2020, focuses finding combination variables predicting temperatures. The densely populated city, faced severe consequences due extreme climate conditions recent years, will pave new dimension further regarding topic.

Language: Английский

---

Applicability of Machine Learning to Predict the Flexural Stresses in Jointed Plain Concrete Pavement

Journal of structural design and construction practice., Journal Year: 2024, Volume and Issue: 30(1)

Published: Oct. 12, 2024

Language: Английский

---

Cross-Sectional Industrialization Factors’ Contribution to Heat Wave Risk Classification

Published: Sept. 27, 2024

Language: Английский

---