Optimized stochastic methods for sensitivity analysis for large-scale air pollution model

Abstract

ÐEnvironmental security is rapidly becoming a significant topic of present interest all over the world, and environmental modelling has a very high priority in various scientific fields, respectively. Different optimizations of the Latin Hypercube Sampling algorithm have been used in our sensitivity studies of the model output results for some air pollutants with respect to the emission levels and some chemical reactions rates.

Analyzing Tehran’s Air Pollution Using System Dynamics Approach

“…INSPiRE: an integrated approach to tackling household air pollution and improving health in rural Cambodia Chandna and Honney, 2017 [8] Long-term exposure to urban air pollution and the relationship with life expectancy in cohort of 3.5 million people in Silesia Dziubanek et al, 2017 [9] Highway toll and air pollution: evidence from Chinese cities Fu and Gu, 2014 [10] Estimation of spatial patterns of urban air pollution over a 4-week period from repeated 5-min measurements Gillespie et al, 2016 [11] Chemical characteristics of atmospheric PM2.5 loads during air pollution episodes in Giza, Egypt Hassan and Khoder, 2017 [12] Urban air pollution from the open burning of municipal solid waste Okedere et al, 2019 [6] Optimized stochastic methods for sensitivity analysis for large-scale air pollution model Todorov, V. et al, 2021 [13] Advanced stochastic approaches for Sobol' sensitivity indices evaluation Todorov, V. et al, 2021 [14] Hence, it is imperative to constantly monitor the level of pollutants in the air and ensure that air quality meets the standards [15]. Air quality in cities results from a complicated interaction between environmental conditions and human factors [3].…”