The formulation of an adequate and practical Atmospheric Air Quality Management Plan at different spatial scales at local (micro), city (medium), national (macro)), and temporal (short and long term) is an indispensable solution to prevent the public from air pollution health risk. The air quality monitoring system provides regulatory agencies a comprehensive data of current air contaminants in a particular location. Then, air monitoring data of pollutants is processed into a dimensionless unit called the "Air Quality Index" (AQI); it serves as an information medium for the people to know the air quality health of their location and takes preventative steps accordingly (public participation). Thus, the AQI is a beneficial tool for the public, stakeholders, and regulators to understand the current state of air quality. AQI across the globe considers the number of pollutants (most of the developed countries and some developing countries considers PM to measure the overall status of air quality being monitored), averaging time for which pollutants are measured, calculation method to compute air quality indices for each pollutant, calculation mode to aggregate the overall index, scale of an index, categories, colour coding scheme, and related descriptive terms of the pollutants. This article presents rationalized and extensive reviews of various Air Quality Index (AQI) models utilized worldwide from 1960 to 2021, comparing them based on several parameters such as types and number of pollutants (criteria or hazardous air pollutants), averaging time (long-term or short-term), calculation methods (linear or nonlinear), calculation modes [single-pollutant (maximum value) or multi-pollutants (combined effect)]. By analysing the strengths and flaws of all the AQI models developed so far, it is recommended to develop a more reliable, extensible, and comparable AQI model to be employed as an executive tool for designing strategic pollution abatement programs to preserve public health.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s10661-022-09896-8 | DOI Listing |
Genet Epidemiol
January 2025
Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.
Gene-environment interactions have been observed for childhood asthma, however few have been assessed in ethnically diverse populations. Thus, we examined how polygenic risk score (PRS) modifies the association between ambient air pollution exposure (nitrogen dioxide [NO], ozone, particulate matter < 2.5 and < 10 μm) and childhood asthma incidence in a diverse cohort.
View Article and Find Full Text PDFSci Data
January 2025
Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Hubei Hongshan Laboratory, Key Laboratory of Aquaculture Disease Control, Ministry of Agriculture and Rural Affairs, The Innovation Academy of Seed Design, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
The large-scale loach (Paramisgurnus dabryanus; Cypriniformes: Cobitidae) is primarily distributed in East Asia. It is an important economic fish species characterized by fast growth, temperature-dependent sex determination and the ability to breathe air. Currently, molecular mechanism studies related to some aspects such as sex determination, toxicology, feed nutrition, growth and genetic evolution have been conducted.
View Article and Find Full Text PDFEnviron Res
January 2025
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM machine-learning model covering the contiguous US from 2003 through 2023. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures.
View Article and Find Full Text PDFEnviron Res
January 2025
Department of Epidemiology, NUTRIM School for Translational Research in Metabolism, Maastricht University Medical Centre, P.O. Box 616, 6200 MD Maastricht, The Netherlands. Electronic address:
Prenatal exposure to air pollution has been linked to lower birth weight, yet the role of the placenta in this association is often overlooked. This study investigates whether placental characteristics act as moderators or mediators in the association between prenatal exposure to particulate matter (PM) and nitrogen dioxide (NO) and birth weight in twins. The study included 3340 twins (born 2002-2013) from the East Flanders Prospective Twin Survey.
View Article and Find Full Text PDFEnviron Pollut
January 2025
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. Electronic address:
PNPLA3-I148M genotype is the strongest predictive single-nucleotide polymorphism for liver fat. We examine whether PNPLA3-I148M modifies associations between oxidative gaseous air pollutant exposure (O) with i) liver fat and ii) multi-omics profiles of miRNAs and metabolites linked to liver fat. Participants were 69 young adults (17-22 years) from the Meta-AIR cohort.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!