A review of AirQ Models and their applications for forecasting the air pollution health outcomes

Abstract

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

Cookstove Smoke Impact on Ambient Air Quality and Probable Consequences for Human Health in Rural Locations of Southern Nepal

“…[53,54]. The AirQ+ also helps to analyze the impacts on population in different emission scenarios [31]. Unlike other tools developed for the health risk assessments such as AirCounts, Aphekom, Economic Valuation of Air Pollution (EVA), SIM-Air etc, the AirQ+ can be used for any population size of the specified area with mortality and morbidity characteristics [55].…”

“…The Air Q+ model also gives the user options to run different scenarios to understand the health burden originating due to a particular air pollutant and for a particular type of disease in a given age group. Even the concentration response functions used for calculation of different health burdens by this tool have been well validated across different epidemiological studies [31]. Additionally, the Urban Health Initiative (UHI) program launched by the WHO in developing countries is supporting policy makers to understand the health effects and associated economic burdens of different pollutants.…”

“…Moreover, various tools/software such as AirQ, BenMap, AirQ+, etc. have been developed to understand the health impacts of air pollution [30,31]. Different air pollutants (PM 2.5 , PM 10 , NO 2 , and O 3 ) can be used as input to these tools to derive acute and chronic health outcomes of air pollution exposure.…”

Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models

“…Describing the pathways from the generation of emission, dispersion and chemical transformation of pollutants in ambient air is very challenging because of its high variability over space and time [13]. To represent this intraurban variability in pollutant concentrations, various sophisticated exposure assessment methods were used in the recent past [9,14]. There are also several research efforts investigating air pollution modelling approaches using machine learning and other computationally intensive methods [15][16][17].…”

Monitoring and Evaluation of Terni (Central Italy) Air Quality through Spatially Resolved Analyses

“…One of the most efficient tools for the formulation of better control strategies for the atmospheric pollutant is the PM source apportionment, relevant to identify the role of different particulate matter emission sources. Typically, in air pollution control, the dispersion of air pollutants released from different emission sources are estimated through atmospheric dispersion mathematical models, without using wide and expensive monitoring networks [3,4]. However, it is important to consider the limitations of mathematical models [5][6][7][8].…”