Advanced searches left 3/3

Air Pollution - Zenodo

Summarized by Plex Scholar
Last Updated: 08 August 2022

* If you want to update the article please login/register

Urban Policy Interventions to Reduce Traffic-Related Emissions and Air Pollution: A Systematic Evidence Map

Background Urban areas are the most vulnerable areas for human exposure to air pollution, which arises in large part from traffic. As the urban population continues to grow, a greater number of people are vulnerable to traffic-related air pollution and its associated, costly health risks. This systematic evidence map examines and assesses peer-reviewed research on urban-level policy efforts aimed at reducing traffic emissions and/or TRAP from on-road smartphone sources, thus potentially lowering human exposures and adverse health effects, as well as manufacturing various co-benefits. Based on previously established eligibility criteria, Covidence was used to screen papers for inclusion at the title/abstract and full text levels. The final SEM found 7528 unique papers from database searches, as well as 365 unique ones. There were 58 unique policy proposals and a total of 1,092 unique policy scenarios. For example, the complete implementation of the most advanced technical steps to reduce emissions in Gdansk and Katowice, Poland, from 16-53 g/m 3 to 0. 2-2. 4 g/m 3, and 3 – 4. This is the first peer-reviewed SEM to compile international evidence on urban-level policy changes to minimize traffic emissions and/or TRAP in the context of human exposure and health effects, as well as reporting unknown enablers, limits, and co-benefits. A systematic evidence map will be used by a city policy initiative to minimize traffic emissions and traffic-related air pollution.

Source link: https://zenodo.org/record/6937363


EDUCATION- AN AIR POLLUTION CONTROL DEVICE

Human beings in the 21st century have brought about dramatic changes in virtually every facet of life. paraphrasedoutput: There have been major changes in the quality of life and living conditions in the wake of the industrial revolution. Human beings have disrupted nature itself thanks to the excessive use of scientific study and the desire for material pleasure. nbsp; paraphrasedoutput: In the near future, the population is expected to grow at a faster rate, and man's needs are expected to rise even faster.  Pollution will be the biggest issue of the 21st century, Pollution will be the biggest issue of the 21st century, .

Source link: https://zenodo.org/record/6900879


COVID-19 and Climate Change: Determinants of Air Pollution in Six Metro Cities of India

This paper introduces a new way of measuring, modeling, and reporting air pollution in the context of COVID-19 growth and monitoring, meteorological variables, and unlockdown. The report is based on real data from 25 March 2020 to 19 October 2020 on COVID-19 cases and air pollution in six metro cities in India's six metro cities. The paper provides city-wise Air Pollution Indices based on PM2. 5, SO2, CO2, and NO2 constituents. It models the influence of the growth of COVID-19, lockdown/unlockdown, and three climatic variables that determine air pollution, namely, temperature, wind speed, and humidity. Within the six metro cities, there are significant differences, with Delhi being the poorest in terms of air pollution considering four specific air pollutants. COVID-19 reached its high point on 18 September 2020 during the first wave, according to another study. Wind speed and humidity reduce air pollution, although extreme temperatures raise air pollution.  .

Source link: https://zenodo.org/record/6837399


Building a model for choosing a strategy for reducing air pollution based on data predictive analysis

This paper formalizes the procedure of choosing a pollution reduction program in urban setting. The model involves determining the optimal location of biotechnological devices; biotechnological filter systems; or smart air purification devices based on solving the problem of discrete optimization, taking into account the forecast of the air quality index. The combined model software suite includes the EMD-ESM hybrid model, the HWM additive model, and the adaptive TLM. EMD-ESM model has an edge in the case of short-term forecasting of the air quality index time series, according to the criterion of a minimum root mean square error;=0. 11. The findings presented here are input data for the task of choosing air pollution reduction techniques in the urban environment.

Source link: https://zenodo.org/record/6823299

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions