Visibility Driven Perception and Regulation of Air Pollution in Hong Kong, 1968–2020

Fifty-year change in air pollution in Kaohsiung, Taiwan

The change in air quality in cities can be the product of regulation and emissions. Regulations require enforcement of emission reduction, but it is often shifting economic and societal structures that influence pollutant emissions. This study examines the long-term record of air pollutants in Kaohsiung, where post-war industrialisation increased pollution substantially, although improvements are observed in recent decades as the city moved to a more mixed economy. The study tracks both gases and particles across a period of significant change in pollution sources in the city. Concentrations of SO₂ and aerosol SO₄^2- were especially high ~1970, but these gradually declined, although SO₄^2- to a lesser extent than its precursor, SO₂. While twenty-first century emissions of SO₂ and NO_x have declined, this has been less so for NH₃, because it arises from predominantly agricultural sources. The atmosphere in Kaohsiung continues to have high concentrations of O₃, and these have risen in the city, likely a product of less titration by NO. The changes have meant that ozone has become an increasing threat to health and agriculture. Despite a potential for producing (NH₄)₂SO₄ and NH₄NO₃ aerosols, a product of a relatively constant supply of NH₃, visibility has improved in recent years. Emissions of SO₂ and NO_x should continue to be reduced, as these strongly affect the amount of fine secondary aerosol. However, the key problem may be ozone, which is difficult to control as it requires careful consideration of the balance of NO_x and hydrocarbons so important to its production.

Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations

Fine particulate matter (PM2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.

Poor Air Quality in Urban Settings: A Comparison of Perceptual Indicators, Causes and Management in Two Cities

Poor air quality (PAQ) is a global concern, especially in urban areas, and is often seen as an important element of social sustainability given its negative impact on health and quality of life. However, little research has been undertaken in cities of the developing world to explore how residents perceive poor air quality, its main causes, what control measures should be used to address PAQ and where the main responsibility rests for implementing control measures. The research described in this paper sought to address these points, using a questionnaire-based survey (n = 262) in Nigeria’s federal capital city of Abuja (n = 137) and the state-capital city of Enugu (n = 125). The survey took place during the COVID-19 pandemic (October 2020 to March 2021), and was stratified to ensure representation across a number of demographic groups such as gender, age, education and income. The results were analysed using the Kruskal–Wallis non-parametric test and Hochberg’s post hoc test available in SPSS version 28. The study found that the ranking of perceptual indicators and the main causes of PAQ had much agreement between respondents from both cities and between demographic groups. Smoke, odour and dust particles were perceived to be the most important indicators of PAQ, while the main sources of PAQ were waste and bush burning, vehicle use and power generators. The two most preferred control measures were proper waste management and the avoidance of bush burning. However, there was a significant difference between the two cities in terms of the main organisations responsible for addressing PAQ, with respondents from Abuja citing the federal government, while those from Enugu cited the state government. Interestingly, younger people in Enugu noted that the government should take more responsibility in controlling PAQ than did the older demographic in that city, but this difference was not seen in Abuja. Overall, this study reveals that residents in these two Nigerian cities clearly recognise their exposure to PAQ and it suggests that these perceptual indicators, and views on sources and interventions should be central to designing policies to control this important issue.

Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression

Mitigation measures and control strategies relating to the novel coronavirus disease 2019 (COVID-19) have been widely applied in many countries to reduce the transmission of this pandemic disease. China was the first country to implement a strong lockdown policy to control COVID-19 when countries worldwide were struggling to manage COVID-19 cases. However, lockdown causes numerous changes to air-quality patterns due to the low amount of traffic and the decreased human mobility it results in. To study the impact of the strict control measures of the new COVID-19 epidemic on the air quality of Hubei in early 2020, the air-quality monitoring data of Hubei’s four cities, namely Huangshi, Yichang, Jingzhou, and Wuhan, from 2019 to 2021, specifically 1 January to 30 August, was examined to analyze the characteristics of the temporal and spatial distribution. All air-quality pollutants decreased during the active-COVID-19 period, with a maximum decrease of 26% observed in PM10, followed by 23% of PM2.5, and a minimum decrease of 5% observed in O3. Changes in air pollutants from 2017 to 2021 were also compared, and a decrease in all pollutants through to 2020 was found. The air-quality index (AQI) recorded an increase of 2% post-COVID-19, which shows that air quality will worsen in future, but it decreased by 22% during the active-COVID-19 period. A path analysis model was developed to further understand the relationship between the AQI and air-quality patterns. This path analysis shows a strong correlation between the AQI and PM10 and PM2.5, however its correlation with other air pollutants is weak. Regression analysis shows a similar pattern of there being a strong relationship between AQI and PM10 (r2 = 0.97) and PM2.5 (r2 = 0.93). Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, it is likely that the reduction in air pollution and the significant improvement in ambient air quality due to lockdowns provided substantial short-term health benefits. The government must implement policies to control the environmental issues which are causing poor air quality in post-COVID-19.