Co-occurrence of urban heat and the COVID-19: Impacts, drivers, methods, and implications for the post-pandemic era

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air quality monitoring stations in Beijing from 2015 to 2022, this study first employs a deweathering machine learning technique to decouple the confounding effects of meteorological on the air pollution. Furthermore, a detrending percentage change indictor is applied to remove the influence of seasonal variations on air pollution. The findings reveal that: (1) Human interventions are the primary drivers of changes in air pollution concentrations, whereas meteorological factors have a relatively minor impact. (2) During the COVID-19 lockdown, significant variations in air pollution levels are observed, with the effects of city lockdown ranging from a decrease of 40.11% ± 14.81% to an increase of 20.28% ± 14.36%. Notably, there is a decline in concentrations of NO2, PM2.5, CO, and PM10, while the levels of O3 and SO2 increase even during the strictest lockdown period. (3) In the year following the COVID-19 lockdown, there is a rebound in overall air pollution levels. However, by the second year, a general decline in air pollution is observed, except for O3. Therefore, it is imperative to integrate the confounding effects of meteorological factors into air quality management policies under various future scenarios: adopt high-intensity control measures for sudden air quality deteriorations, advance green recovery initiatives for long-term emission reductions, and coordinate efforts to reduce composite atmospheric pollution.

Impact of aerosol concentration changes on carbon sequestration potential of rice in a temperate monsoon climate zone during the COVID-19: a case study on the Sanjiang Plain, China

The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.

Assessment of vertical cooling performance of trees over different surface covers

Tree-induced cooling benefits are associated with various factors, such as canopy morphology, surface cover, and environmental configuration. However, limited studies have analyzed the sensitivity of tree-induced cooling effects to the combination of such factors. Most studies have focused on 1.5-m cooling performance, and few studies on the variability of the under-tree vertical cooling performance. Therefore, this study aims to investigate the vertical cooling performance of different combinations of trees and surface covers. The study was completed in Chongqing, China, with field experiments capturing vertical air temperature and wind speed at 0.5, 1.0, 1.5, 2.0 and 2.5 m under two typical combinations of "tree + grass" (ComA) and "tree + shrubs" (ComB), and capturing 1.5 m microclimatic environments of a control group with hard pavement without tree shade (REF). The results show that at an average ambient temperature of 33 °C, the maximum air-cooling temperatures for ComA and ComB were 2.46 °C and 1.78 °C, respectively. An increase in the ambient temperature corresponded to a decrease in the cooling effect difference between ComA and ComB. ComA had a maximum vertical temperature difference of 1.01 °C between H1.5m and H2.0m. Between H2.5m and H2.0m, the maximum vertical temperature difference for ComB was 1.64 °C. This study explored the changing patterns of under-tree vertical temperatures under different tree and surface cover combinations, conducive to clarifying the key elements affecting tree cooling performance. The results have implications for accurate thermal comfort assessments and provide a theoretical basis for fine-tuning the design of under-tree spaces.

Heat waves characteristics intensification across Indian smart cities

Indian cities have frequently observed intense and severe heat waves for the last few years. It will be primarily due to a significant increase in the variation in heat wave characteristics like duration, frequency, and intensity across the urban regions of India. This study will determine the impact of future climate scenarios like SSP 245 and 585 over the heat wave characteristics. It will present the comparison between heat waves characteristics in the historical time (1981 to 2020) with future projections, i.e., D1 (2021-2046), D2 (2047-2072), and D3 (2073-2098) for different climate scenarios across Indian smart cities. It is observed that the Coastal, Interior Peninsular, and North-Central regions will observe intense and frequent heat waves in the future under SSP 245 and 585 scenarios. A nearly two-fold increase in heat wave' mean duration will be observed in the smart cities of the Interior Peninsular, Coastal, and North Central zones. Thiruvananthapuram city on the west coast has the maximum hazard associated with heat waves among all the smart cities of India under both SSPs. This study assists smart city policymakers in improving the planning and implementation of heat wave adaptation and mitigation plans based on the proposed framework for heat action plans and heat wave characteristics for improving urban health well-being under hot weather extremes in different homogeneous temperature zones.

Synergistic effect of pollution reduction and carbon emission mitigation in the digital economy

Synergetic control of environmental pollution and carbon emissions (SCEPCE) is essential to green development. The emergence of the digital economy has become a significant component in regional economic growth. Investigating the digital driving mode for SCEPCE in developing countries is crucial. This paper empirically analyzes the effect of establishing big data comprehensive experimental areas (BDCEAs) on air pollutants and carbon emissions using panel data of prefecture-level cities from 2009 to 2020 and the time-varying difference-in-differences method. The research found that (1) BDCEA inhibits pollution and carbon emissions, and the policy effect is sustainable. (2) The synergistic effect is significant, particularly in small and medium-sized cities and old industrial-base cities. The benefit of reducing pollution is only significant in the east. The effect of reducing CO₂ emissions is only significant in the west. (3) The pollution reduction effect of digital economic development has the characteristics of an increasing marginal effect, and the marginal effect of its carbon reduction effect is not apparent. (4) The technological innovation and energy efficiency improvement effects are effective mechanisms. This paper enriches the studies on the factors influencing SCEPCE, which will help to realize SCEPCE and the harmonious coexistence of humans and nature in developing countries. However, policy incentives and green development strategies must be fine-tuned to achieve global SCEPCE.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Government is expected to lead the payment of heat-resilient infrastructure

Urban heat is severe in numerous cities, but the urgency of heat action and support for the development of heat-resilient infrastructure is unclear. To address these research gaps, this study investigated the perceived urgency of developing heat-resilient infrastructure and associated payment issues in eight megacities, in China using a questionnaire survey of 3758 respondents in August 2020. Overall, the respondents thought it was moderately urgent to take actions to address heat-related challenges. The development of mitigation and adaptation infrastructure is urgent. About 86.4% of the 3758 respondents expected the government to be involved in paying for heat-resilient infrastructure, but 41.2% supported cost-sharing among the government, developers, and owners. There were 1299 respondents willing to pay, resulting in an average annual payment of 44.06 RMB in a conservative scenario. This study is important for decision-makers to formulate plans on heat-resilient infrastructure and to release financial strategies for collecting investments and funds.