A proposed population-health based metric for evaluating representativeness of air quality monitoring in cities: Using Hong Kong as a demonstration

Abstract

City air quality monitoring (AQM) network are typically sparsely distributed due to high operation costs. It is of the question of how well it can reflect public health risks to air pollution given the diversity and heterogeneity in pollution, and spatial variations in population density. Combing high-resolution air quality model, spatial population distribution and health risk factors, we proposed a population-health based metric for AQM representativeness. This metric was demonstrated in Hong Kong using hourly modelling data of PM10, PM2.5, NO2 and O3 in 2019 with grid cells of 45m * 48m. Individual and total hospital admission risks (%AR) of these pollutants were calculated for each cell, and compared with those calculated at 16 monitoring sites using the similarity frequency (SF) method. AQM Representativeness was evaluated by SF and a population-health based network representation index (PHNI), which is population-weighted SF over the study-domain. The representativeness varies substantially among sites as well as between population- and area-based evaluation methods, reflecting heterogeneity in pollution and population. The current AQM network reflects population health risks well for PM10 (PHNI = 0.87) and PM2.5 (PHNI = 0.82), but is less able to represent risks for NO2 (PHNI = 0.59) and O3 (PHNI = 0.78). Strong seasonal variability in PHNI was found for PM, increasing by >11% during autumn and winter compared to summer due to regional transport. NO2 is better represented in urban than rural, reflecting the heterogeneity of urban traffic pollution. Combined health risk (%ARtotal) is well represented by the current AQM network (PHNI = 1), which is more homogenous due to the dominance and anti-correlation of NO2 and O3 related %AR. The proposed PHNI metric is useful to compare the health risk representativeness of AQM for individual and multiple pollutants and can be used to compare the effectiveness of AQM across cities.

Estimation of On‐Road PM2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

“…In the headed study, hourly PM 2.5 concentration data, from 1 January 2014 to 31 December 2021, were obtained from all 16 air quality monitoring stations (AQMS) in Hong Kong (Figure 1 ). These hourly PM 2.5 concentrations are based on high‐accuracy gravimetric sampling, using the Thermal Scientific Model 1405‐DF Filter Dynamics Measurement System (FDMS) from the Hong Kong Environment Protection Department (HKEPD) (Hohenberger et al., 2021 ; G. Shi et al., 2021 ).…”

Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China

“…Another health risk study concluded that the AQHI system in Hong Kong helped to reduce the cardiovascular hospitalization for elderly population ( Mason et al, 2020 ). This health risk model was also applied to evaluate the representativeness of air quality monitoring network ( Hohenberger et al, 2021 ) and estimate the high-resolution exposure distribution ( Che et al, 2020 ).…”