Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

Abstract

Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.

Holistic One Health Surveillance Framework: Synergizing Environmental, Animal, and Human Determinants for Enhanced Infectious Disease Management

“…137 An Environmental Health Surveillance or tracking system may be defined as a system that performs the continuous collection, integration, analysis, and interpretation of data on human health effects relating to exposure to environmental hazards. 138 By meticulously monitoring environmental indicators, such as air quality, water quality, climate patterns, and ecosystem dynamics, we can uncover the environmental drivers that shape infectious disease dynamics. Environmental Health Surveillance offers insights into the geographic distribution of diseases, the impact of climate change on disease transmission, and the identification of environmental hotspots.…”

A Scoping Review on Wearable Devices for Environmental Monitoring and Their Application for Health and Wellness

“…Regarding the outdoor environment, different studies highlighted the strong spatial variability of air pollution over a short distance (<100 m) [37], suggesting a paradigm shift from constant air quality characterization to a variable in time and space characterization. In fact, air pollution is strongly dependent on pollution sources and air flow, which is also difficult to predict with a sophisticated numerical modeling [38]. For instance, in an urban area where high buildings are present on both sides of a street, air can be strongly polluted because low air dispersion is allowed and, thus, high local pollution can be detected, but if the nearest fixed station is not in this "street canyon", data extracted about pollution cannot be representative of the situation in that street.…”

“…A possible way to cope with this issue is to evaluate the ratio between indoor/outdoor pollution and then using outdoor data to evaluate indoor pollution, adding the contribution of sources or ventilation [38]. This means that time-activity diaries, Artificial Neural Networks [42] or GPS are necessary to retrieve the trajectories of an individual.…”

Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling

“…One practical application is the analysis and assessment of traffic noise contamination [23]. In other research, noise mapping is performed through interpolating data from different monitoring stations where noise pollution can then be assessed through equivalent sound pressure data [24]. An example of such work was done by Harman et al [25] where the noise map of Isparta city was generated using inverse distance weighted (IDW), Kriging, and multiquadric interpolation methods using various parameters.…”