Rapid urbanisation has altered the vulnerability of urban areas to heat wave disasters. There is an urgent need to identify the factors underlying the effect of heat waves on human health and the ...areas that are most vulnerable to heat waves. In this study, we plan to integrate indices associated with heat wave vulnerability based on meteorological observation data, remote sensing data and point of interest (POI) data; analyse the influence of urbanisation on the urban vulnerability environment; and explore the relationship between the vulnerability environment and heat-wave-related mortality. Finally, we attempt to map the spatial distribution of high heat-wave-related mortality risk based on the results of heat wave vulnerability study and artificial society. The results reveal that 1) there are differences in the influence of urbanisation on heat wave exposure, sensitivity and adaptability; 2) the exposure and sensitivity level effects on the lower limit of health impacts and the adaptability level effects on the upper limit of the health impact from heat wave in a given study area; and 3) areas vulnerable to the effects of heat waves are not confined to the city centre, which implies that residents living in suburban areas are also vulnerable to heat waves. Finally, this study not only explores the factors contributing to the impacts of heat waves but also describes the spatial distribution of the risk of disaster-associated mortality, thereby providing direct scientific guidance that can be used by cities to address heat wave disasters in the future.
Abstract
Heat-induced labor loss is a major economic cost related to climate change. Here, we use hourly heat stress data modeled with a regional climate model to investigate the heat-induced labor ...loss in 231 Chinese cities. Results indicate that future urban heat stress is projected to cause an increase in labor losses exceeding 0.20% of the total account gross domestic product (GDP) per year by the 2050s relative to the 2010s. In this process, certain lower-paid sectors could be disproportionately impacted. The implementation of various urban adaptation strategies could offset 10% of the additional economic loss per year and help reduce the inequality-related impact on lower-paid sectors. So future urban warming can not only damage cities as a whole but can also contribute to income inequality. The implication of adaptation strategies should be considered in regard to not only cooling requirements but also environmental justice.
To characterize the air pollutants in Shanghai Port and identify the contribution from ship traffic emission, field measurements have been conducted in 2011. The trace gases SO2, NO2 and O3 were ...monitored and aerosol samples of TSP, PM2.5 and size-segregated particles were collected in a working area of Shanghai Port. Elements including V, Ni, Al, Fe, Si, Ca, Na, Mg, Mn, Zn, Co, Cr in aerosol samples and heavy fuel oil samples were analyzed. The results revealed that average hourly SO2 and NO2 concentrations in Shanghai Port were respectively 29.4 and 63.7 μg m−3, average daily concentrations of TSP and PM2.5 were 114.39 and 62.60 μg m−3, comparable with the ones in Shanghai land area. Ni and V were found enriched in fine particles with averaged concentrations of 80.0 and 14.8 ng m−3 in PM2.5 respectively. Also ratio of V/Ni in aerosol under summertime airflow was 3.4, very close to the ratio of averaged V and Ni content in international heavy fuel oils used in Shanghai Port. The backward trajectory analysis further revealed that SO2, NO2, and V under coastal airflows were mainly from ship traffic emission. The mean concentration of V was 15.84 ng m−3 under hybrid coastal airflows, much higher than that of 9.84 ng m−3 under continental airflows. Furthermore, V was found to be highly correlated with ship fluxes, and was selected as an indicator of ship traffic emission in Shanghai. The estimated primary PM2.5 contribution from ship traffic ranged from 0.63 to 3.58 μg m−3, with an average of 1.96 μg m−3. This PM2.5 fraction accounted for 4.23% of the total PM2.5 in an average level, and reached to a maximum of 12.8%. Furthermore, there could be 64% of primary PM2.5 contributed by ships in Shanghai Port transported to inland region. Our results suggest that ship traffic has a non-negligible contribution on ambient levels of fine particles and secondary contribution of SO2 and NO2 emitted by ships need to be estimated on local and regional scale in future.
► Air pollution including gas and aerosol in China's largest port was reported firstly. ► Heavy fuel oil samples were collected and analyzed for multiple elemental contents. ► Contributions of ship emission on PM2.5 in port and inland sites were estimated. ► More than 60% of ship emission could be transported to inland region.
Atmospheric deposition of nitrogen to the eastern China seas has been simulated using the MM5/CMAQ model with the 2004 national emission inventory of China. Dry and wet fluxes are 0.05–0.5 and ...0.2–0.6 g m−2 yr−1, respectively, with the wet deposition accounting for 79% of the total. The total deposition of inorganic nitrogen species, including NO3−, NH4+, HNO3, NOx, and NH3, to the eastern China seas is 498 GgN yr−1 and accounts for 3.4% of the nitrogen emission by China. Deposition of NO3− and NH4+ dominates. The model results agree well with available in situ measurements. The deposition of NH4+ and NH3 to the East China Sea is up to 166 GgN yr−1, which nearly equals the total input of 184 GgN yr−1 from the mainland, including riverine discharge, industrial wastewater, and domestic wastewater. Deposition of atmospheric ammonium can account for 56% of the external total input, which is 1.1–1.5 times the input from the major rivers to all the eastern China seas. Ammonium deposition to the Yellow Sea accounts for as much as 87% of the total input. The annual total nitrogen deposition can be converted to new primary biological productivity of 100–200 mmol C m−2 yr−1, or 1.1–3.9% of the new productivity in the East China Sea. Our results suggest that atmospheric deposition has important impact on biological productivity in all the eastern China seas.
Presently, the harm to human health created by air pollution has greatly drawn public attention, in particular, vehicle emissions including nitrogen oxides as well as particulate matter. How to ...predict air quality, e.g., pollutant concentration, efficiently and accurately is a core problem in environmental research. Developing a robust air quality predictive model has become an increasingly important task, holding practical significance in the formulation of effective control policies. Recently, deep learning has progressed significantly in air quality prediction. In this paper, we go one step further and present a neat scheme of masked autoencoders, termed as masked air modeling (MAM), for sequence data self-supervised learning, which addresses the challenges posed by missing data. Specifically, the front end of our pipeline integrates a WRF-CAMx numerical model, which can simulate the process of emission, diffusion, transformation, and removal of pollutants based on atmospheric physics and chemical reactions. Then, the predicted results of WRF-CAMx are concatenated into a time series, and fed into an asymmetric Transformer-based encoder–decoder architecture for pre-training via random masking. Finally, we fine-tune an additional regression network, based on the pre-trained encoder, to predict ozone (O 3) concentration. Coupling these two designs enables us to consider the atmospheric physics and chemical reactions of pollutants while inheriting the long-range dependency modeling capabilities of the Transformer. The experimental results indicated that our approach effectively enhances the WRF-CAMx model’s predictive capabilities and outperforms pure supervised network solutions. Overall, using advanced self-supervision approaches, our work provides a novel perspective for further improving air quality forecasting, which allows us to increase the smartness and resilience of the air prediction systems. This is due to the fact that accurate prediction of air pollutant concentrations is essential for detecting pollution events and implementing effective response strategies, thereby promoting environmentally sustainable development.
Remote sensing semantic change detection (SCD) involves extracting information about changes in land cover/land use (LCLU) within the same area at different times. This issue is of crucial ...significance in many Earth observation tasks, such as precise urban planning and natural resource management. However, the current methods primarily focus on spatial feature extraction, lacking awareness of temporal features. Consequently, there are challenges in extracting change features, making distinguishing intraclass and interclass differences difficult. This also contributes to pseudochange, posing challenges for SCD tasks. To overcome the limitations of existing methods, we present a dual-dimension feature interaction network (DFINet) for SCD. First, to enhance the assessment and perceptual abilities related to intraclass and interclass differences, we introduce a temporal difference feature enhancement (TDFE) module. This module comprehensively captures features from the temporal dimension. Then, to address the interrelation between multitemporal and multilevel features, we investigate the feature selection interaction (FSIA) and interaction attention modules (IAM), which enable multidimensional deep fusion and interaction of change features. This enhances the capacity for information transfer and integration among the features within multitemporal remote sensing images (RSIs). The experimental results demonstrate that, compared to existing methods, the proposed architecture achieves a significant improvement in accuracy. Additionally, the design enhancements added to DFINet boost the practicality of remote sensing SCD, underscoring its substantial research value.
Construction particulate matter is one of the main environmental impact factors in the construction process. Due to the lack of sufficient awareness and understanding of the potential health effects ...of particulate matter by project managers and construction workers, the on-site working environment has not been effectively improved for a long time, and construction workers have been exposed to high particulate matter concentration conditions for physical labor for a long time. The construction site is a special operation scene, and the source and diffusion of particulate matter are a complex physical change process, and the degree of damage to the health of construction workers is closely related to the exposure dose. Thus, suitable quantitative and evaluation methods need to be adopted. The current on-site particulate matter concentration control system lacks technical and data support and cannot support the needs of on-site environmental management. In this paper, three construction sites in different stages of construction in Shanghai were selected to measure the mass concentration of open source particulate matter, and on this basis, the emission factors of particulate matter in different operating areas were calculated. At foundation stage, the emission factor of TSP, PM10, PM2.5 are 0.0214 g/m2·h, 0.0067 g/m2·h, 0.0054 g/m2·h; at main structure stage, the emission factor of TSP, PM10, PM2.5 are 0.0136 g/m2·h, 0.0053 g/m2·h, 0.0041 g/m2·h; at installation and decoration stage, the emission factor of TSP, PM10, PM2.5 are 0.0165 g/m2·h, 0.0059 g/m2·h, 0.0043 g/m2·h. Using simulation software to simulate the temporal and spatial distribution of particulate matter concentration at the site of the example project, it is found that workers engaged in pit bottom operation in the foundation stage, steel bar processing in the main structure stage, and plastering, masonry and putty workers in the installation and decoration stage are the people with the highest occupational health risk at the construction site. In this study, DALYs were used as a metric to monetize the health risks of particulate matter to workers in the field. Support scientific decision-making on particulate matter control at construction sites and improve the level of on-site occupational health management.
In March 2022, a new wave of COVID-19 outbreak occurred in Shanghai due to the widespread transmission of the Omicron variant. A two-month citywide lockdown was implemented from April 1st to May ...31st, adopting measures such as zone-based classification and grid management. This unique social event provided an “ideal air quality experiment” for pollution research. The rapid reduction in economic activities during the lockdown had many positive impacts on the environment, leading to overall improvements in air quality. Particularly, the concentration of NOx, one of the precursors to O3, significantly decreased. However, O3, as a typical secondary pollutant, showed a noticeable increase. This study uses the WRF-CAMx-OSAT air quality model method to analyze the source of O3 pollution in Shanghai from April to May 2022. The impact of O3 precursor control, sector sources, and regional contributions on the formation of O3 pollution in Shanghai is analyzed in depth. During the pandemic lockdown period, it was found that, in Shanghai, the overall O3 levels were controlled by VOCs (Volatile Organic Compounds), and controlling VOCs proved to be an effective measure in reducing O3 concentrations in Shanghai. Compared with the same period in 2021, the proportion of road traffic sources contributing to ozone concentration has significantly decreased from 70.61% to 64.3%, but they are still the largest contributor. The contribution of industrial emissions to the ozone concentration has significantly risen from 20.71% to 26.36%, making them still the second largest contributor. Industrial and traffic sources are emission sources that require particular attention. The contribution ratio of local sources to external transport is about 7:3, which is higher than the ratio of local sources to external transport in the same period of 2021, which is about 6:4. The local ozone is the main source of ozone concentration in Shanghai, and controlling local source emissions is the key to controlling ozone concentration in the Shanghai area. When excluding the impact of long-range transport, the main areas contributing to O3 formation from local sources are Baoshan District, Jiading District, Qingpu District, and Chongming District, accounting for approximately 41.12% of the total absolute contribution. Different source regions exhibit significant spatial variations in their contributions to the ozone concentration. Through these studies, we aim to provide scientific support and control suggestions for the precise prevention and control of O3 pollution in Shanghai.
Many cities are developing mitigation plans in an effort to reduce the population health impacts from expected future increases in the frequency and intensity of heat waves. To inform heat mitigation ...and adaptation planning, information is needed on the extent to which available mitigation strategies, such as reflective and green roofs, could result in significant reductions in heat exposure. Using the Weather Research and Forecasting (WRF) model, we analysed the impact of green and cool (reflective) roofs on the urban heat island (UHI) and temperature-related deaths in the Greater Boston area (GBA) and New England area (NEA) in summer and winter. In the GBA, green and cool roofs reduced summertime population-weighted temperature by 0.35 °C and 0.40 °C, respectively. In winter, green roofs did not affect temperature, whereas cool roofs caused a temperature reduction of 0.40 °C. In the NEA, the cooler summers induced by green and cool roofs were estimated to reduce the heat-related mortality rates by 0.21% and 0.17%, respectively, compared to baseline. Cool-roof-induced temperature reduction in winter could increase the cold-related mortality rate by 0.096% compared to baseline. These results suggest that both green and cool roofing strategies have the potential to reduce the impact of heat on premature deaths. Additionally, the differing effects in winter suggest the need for a careful consideration of health trade-offs in choosing heat island mitigation strategies.
The high spatial resolution of satellite data and the capability of physics-based approaches are considered highly suitable for testing the integration of remote sensing technologies into the water ...quality monitoring of small and medium-sized inland lakes. This research thus aimed to investigate an operational algorithm for chlorophyll-a (Chla) estimation based on China’s recently launched high-spatial-resolution GF-1 satellite data for Lake Dianshan, a eutrophic lake in Shanghai city, eastern China. For the calibration of the empirical model, an enhanced three-band model and an improved four-band model (IFB) developed by model derivation and statistical analysis based on in situ water sampling and satellite reflectance data were proposed. The IFB model could account for more than 90% of the Chla variation in the GF-1 satellite data. For the calibration of the semi-empirical model, the performance of Δ
Φ
and an improved NCI model (NCI’) were analyzed and validated with field spectral measurements and GF-1 satellite data. The corresponding GF-1 satellite Δ
Φ
model and NCI’ model reached high estimation accuracies of
R
2
= 0.80 and 0.76, respectively. The good estimated results indicate that the established GF-1 satellite models are promising and applicable to estimating Chla in small and medium-sized eutrophic inland lakes.
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