Increases in the extent and level of air pollution in Chinese cities have become a major concern of the public and burden on the government. While ample literature has focused on the status, changes and causes of air pollution (particularly on PM2.5 and PM10), significantly less is known on their effects on people. In this study we used Hangzhou, China, as our testbed to assess the direct impact of PM2.5 on youth populations that are more vulnerable to pollution. We used the ground monitoring data of air quality and Aerosol optical thickness (AOT) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the spatiotemporal changes of PM2.5 by season in 2015. We further explored these distributions with land cover, population density and schools (kindergarten, primary school and middle school) to explore the potential impacts in seeking potential mitigation solutions. We found that the seasonal variation of PM2.5 concentration was winter > spring > autumn > summer. In Hangzhou, the percentage of land area exposed to PM2.5 > 50 µg m−3 accounted for 59.86% in winter, 56.62% in spring, 40.44% in autumn and 0% in summer, whereas these figures for PM2.5 of <35 µg m−3 were 70.01%, 5.28%, 5.17%, 4.16% in summer, winter, autumn and spring, respectively. As for land cover, forest experienced PM2.5 of 35–50 µg m−3 (i.e., lower than those of other cover types), likely due to the potential filtering and absorption function of the forests. More importantly, a quantitative index based on population-weighted exposure level (pwel) indicated that only 9.06% of the population lived in areas that met the national air quality standards. Only 1.66% (14,055) of infants and juveniles lived in areas with PM2.5 of <35 µg m−3. Considering the legacy effects of PM2.5 over the long-term, we highly recommend improving the monitoring systems for both air quality and people (i.e., their health conditions), with special attention paid to infants and juveniles.
Albedo is a characterization of the Earth’s surface ability to reflect solar radiation, and control the amount of solar radiation absorbed by the land surface. Within the context of global warming, the temporal and spatial changes of the albedo and its response to climate factors remain unclear. Based on MCD43A3 (V005) albedo and meteorological data (i.e., temperature and precipitation), we analyzed the spatiotemporal variations of albedo (2000–2016) and its responses to climate change during the growing season on the Qinghai-Tibet Plateau (QTP). The results indicated an overall downward trend in the annual albedo during the growing season, the decrease rate was 0.25%/decade, and the monthly albedo showed a similar trend, especially in May, when the decrease rate was 0.53%/decade. The changes also showed regional variations, such as for the annual albedo, the areas with significant decrease and increase in albedo were 181.52 × 103 km2 (13.10%) and 48.82 × 103 km2 (3.52%), respectively, and the intensity of albedo changes in low-elevation areas was more pronounced than in high-elevation areas. In addition, the annual albedo-temperature/precipitation relationships clearly differed at different elevations. The albedo below 2000 m and at 5000–6000 m was mainly negatively correlated with temperature, while at 2000–4000 m it was mainly negatively correlated with precipitation. The contemporaneous temperature could negatively impact the monthly albedo in significant ways at the beginning of the growing season (May and June), whereas in the middle of the growing season (July and August), the albedo was mainly negatively correlated with precipitation, and at the end of the growing season (September), the albedo showed a weak correlation with temperature/precipitation.
As a major component of the north–south transition zone in China, the vegetation ecosystem of the Qinling-Daba Mountains (QBM) is highly sensitive to climate change. However, the impact of sunshine duration, specifically, on regional vegetation remains unclear. By using linear trend, correlation, and multiple regression analyses, this study systematically analyzed the spatiotemporal characteristics and trend changes of the vegetation coverage in the QBM from 2000–2020. Changes in the main climate elements in different periods and the responses to them are also discussed. Over the past 21 years, the vegetation coverage on the east and west sides of the QBM has been lower than that in the central areas. However, it is showing a continuously improving trend, especially in winters and springs. The findings indicate that change of FVC in the QBM exhibited a positive correlation with temperature, a negative correlation with sunshine hours, and both positive and negative correlation with precipitation. On an annual scale, average temperature was the main controlling climatic factor. On a seasonal scale, the area dominated by precipitation in spring was larger. In summer, the relative importance of the three was weak. In autumn and winter, sunshine duration became the main factor affecting vegetation coverage in most areas.
Soil erosion is a serious form of land degradation and poses a considerable threat to food supply, human health, and terrestrial ecosystems globally. The Qinba Mountains are an important geo-ecological transitional zone in China, and quantifying soil erosion in response to climate and land use/land cover (LULC) change can help inform plans for the area’s ecological protection. The spatiotemporal variation of soil erosion intensity in the Qinba Mountains during 2001–2020 was estimated using revised universal soil loss equation (RUSLE). Based on CMIP6 data, combined with Statistical Down Scaling Model (SDSM) and the CA-Markov model, future soil erosion intensity was predicted. The changing trend of soil erosion intensity was compared under four different shared socio-economic pathways (SSPs). The potential contributions of long-term changes in climate and LULC to soil erosion were assessed using statistical methods. The results show that future rainfall and rainfall erosivity will increase by 8%–12% and 3%–14%, respectively. Depending on the different socio-economic pathways, the soil erosion rate will increase by between 12 and 32%, with SSP2-4.5 predicted to cause greatest soil erosion. The analysis of influencing factors showed that rainfall frequency, intensity, and duration could increase the risk of soil erosion, while high temperatures could slow down the erosion rate. Barren land is the most vulnerable to erosion and should continue to be prioritized. Our spatial distribution map of soil erosion risk will help inform sustainable land practices and provide support for adaptation to future ecological environmental hazards caused by climate change.
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