Satellite-Observed Effects from Ozone Pollution and Climate Change on Growing-Season Vegetation Activity over China during 1982–2020

Effects of climate change and ozone on vegetation phenology on the Tibetan Plateau

The vegetation phenology, encompassing the start (SOS) and end (EOS) of the growing season on the Tibetan Plateau, has been significantly impacted by global climate change. Furthermore, ozone (O3) has gradually become the main pollutant in this region, substantially influencing carbon cycle and ecosystems on Earth. While ongoing studies have focused mainly on the implications of climate parameters, including temperature, precipitation, and radiation, the effects of O3 on the SOS and EOS remain unclear. Here, we compared the responses and sensitivities of the SOS and EOS to both climatic factors and O3 in this region. With the use of partial correlation analysis, we found that increased precipitation was the most important factor influencing the SOS and caused earlier occurrence (4.8 % vs. 21.9 %) for most plant functional types. In comparison, temperature only dominated in shrublands. In particular, we found that the EOS responded comparably to climatic factors with similar proportions between advancing and delaying patterns. However, higher O3 levels consistently advanced the EOS for almost all plant functional types and was the main factor controlling EOS variations based on the sensitivity analysis. Our results emphasized that O3 pollution should be considered for obtaining better phenological forecasts and determining the impacts of the environment and atmospheric composition on carbon sequestration in terrestrial ecosystems.

O3 transport characteristics in eastern China in 2017 and 2021 based on complex networks and WRF-CMAQ-ISAM

Increasingly prominent pollution levels and strong regional characteristics of O₃, especially in economically developed eastern China, called for a regional cooperation strategy based on transport quantification. This study adopted the complex networks to construct the O₃ Transport Network (OTN) to explore characteristics in eastern China in the summer of 2017 and 2021, whose results were afterward verified with spatial source apportionment results simulated with WRF-CMAQ-ISAM. As OTN suggested, O₃ transport showed stronger and faster characteristics in eastern China in 2021 than in 2017, judging from changes in the network density, number of connections, transport ranges, and transport paths. Among all cluster communities, inland Shandong was the most important O₃ transport hub, the Central Community was the largest community, and the Southern Community showed the closest inter-city transport relationships. In- and out-weighted degrees in OTN showed relatively superior consistency with the transport matrix obtained with WRF-CMAQ-ISAM, and can be explained by wind fields. Generally, O₃ pollution in the whole eastern China showed more frequent intra-regional transport and more strengthened inter-city correlations in 2021 than in 2017, meanwhile, northerly and southerly cities exhibited strengthening and weakening trends in O₃ transport, respectively. Despite the completely different principles of complex networks and air quality models, their results were mutually verifiable. This study presented a comprehensive understanding of O₃ transport in eastern China for further formulation of regional collaborative strategies and provided the methodological verification for applying complex networks in the atmospheric environment field.

Quantifying changes in soil organic carbon density from 1982 to 2020 in Chinese grasslands using a random forest model

China has the second-largest grassland area in the world. Soil organic carbon storage (SOCS) in grasslands plays a critical role in maintaining carbon balance and mitigating climate change, both nationally and globally. Soil organic carbon density (SOCD) is an important indicator of SOCS. Exploring the spatiotemporal dynamics of SOCD enables policymakers to develop strategies to reduce carbon emissions, thus meeting the goals of "emission peak" in 2030 and "carbon neutrality" in 2060 proposed by the Chinese government. The objective of this study was to quantify the dynamics of SOCD (0-100 cm) in Chinese grasslands from 1982 to 2020 and identify the dominant drivers of SOCD change using a random forest model. The results showed that the mean SOCD in Chinese grasslands was 7.791 kg C m^-2 in 1982 and 8.525 kg C m^-2 in 2020, with a net increase of 0.734 kg C m^-2 across China. The areas with increased SOCD were mainly distributed in the southern (0.411 kg C m^-2), northwestern (1.439 kg C m^-2), and Qinghai-Tibetan (0.915 kg C m^-2) regions, while those with decreased SOCD were mainly found in the northern (0.172 kg C m^-2) region. Temperature, normalized difference vegetation index, elevation, and wind speed were the dominant factors driving grassland SOCD change, explaining 73.23% of total variation in SOCD. During the study period, grassland SOCS increased in the northwestern region but decreased in the other three regions. Overall, SOCS of Chinese grasslands in 2020 was 22.623 Pg, with a net decrease of 1.158 Pg since 1982. Over the past few decades, the reduction in SOCS caused by grassland degradation may have contributed to soil organic carbon loss and created a negative impact on climate. The results highlight the urgency of strengthening soil carbon management in these grasslands and improving SOCS towards a positive climate impact.

Effects of Seasonal Variation on Spatial and Temporal Distributions of Ozone in Northeast China

The levels of tropospheric ozone (O₃) are closely related to regional meteorological conditions, precursor emissions, and geographical environments, which have a significant negative impact on human health. The concentrations of O₃ were relatively low, while the spatial distribution was strongly heterogeneous in Northeast China; however, little is known about how the influencing factors affect the distribution of O₃ in Northeast China. Here, the O₃ concentration, meteorological observation data, precursors (NO₂), and vegetation coverage data from 41 monitoring cities in Northeast China from 2017 to 2020 were collected and analyzed. The spatial-temporal distributions and evolution characteristics of O₃ concentrations were investigated using statistical analysis, kriging interpolation, spatial autocorrelation analysis, cold-hot spot analysis, and geographic detectors, and the effects of meteorological factors, NO₂, and green land area on O₃ concentrations were evaluated seasonally and spatially. The results showed that O₃ pollution in Northeast China was generally at a relatively low level and showed a decreasing trend during 2017-2020, with the highest concentrations in the spring and the lowest concentrations in the autumn and winter. May-July had relatively high O₃ concentrations, and the over-standard rates were also the highest (>10%). The spatial distribution showed that the O₃ concentration was relatively high in the south and low in the northeast across the study area. A globally significant positive correlation was derived from the spatial autocorrelation analysis. The cold-hot spot analysis showed that O₃ concentrations exhibited spatial agglomerations of hot spots in the south and cold spots in the north. In Northeast China, the south had hot spots with high O₃ pollution, the north had cold spots with excellent O₃ levels, and the central region did not exhibit strong spatial agglomerations. A weak significant negative correlation between O₃ and NO₂ indicated that the emissions of NOx derived from human activities have weak effects on the O₃ concentrations, and wind speed and sunshine duration had little effect on spatial differentiation of the O₃ concentrations. Spatial variability in O₃ concentrations in the spring and autumn was mainly driven by temperature, but in the summer, the influence of temperature was weakened by the relative humidity and precipitation; no factor had strong explanatory power in the winter. The temperature was the only controlling factor in hot spots with high O₃ concentrations. In cold spots with low O₃ concentrations, the relative humidity and green land area jointly affected the spatial distributions of O₃.

Spatio-temporal variation of ozone pollution risk and its influencing factors in China based on Geodetector and Geospatial models

Ozone (O₃) has become the primary pollutant in many cities, and high concentrations of O₃ cause significant harm to the ecological environment and human health. This study investigated the spatiotemporal distribution of surface concentrations of ozone over entire China and analyzed the influencing factors based on the geographical detector technique. Moreover, the Pearson correlation analysis was used to analyze the influence of various meteorological factors on ozone concentrations. The results showed that, on the national scale, the daily average O₃ concentration in the cities of China in 2019 was 92.441 μg/m³ and the nonattainment rate of daily average ozone was 7.98%. However, the ozone nonattainment rate was 33.33% in heavily polluted regions. The highest O₃ concentration was observed in summer, and the lowest was observed in spring. The O₃ concentrations in cities across the country showed significant spatial distribution characteristics. Among the five pollutants, the highest correlation was observed between O₃ and PM_2.5 and the lowest was observed between O₃ and SO₂. Among the metrological factors, wind speed and solar radiation are the most influencing factors, and showed positive correlation. Moreover, the annual precipitation is negatively correlated with O₃-8h concentrations. The methods and findings of this paper can be used as an aid for air pollution control programs in different regions for diminishing the risk of exposure to various air pollutants.

Factors Influencing PM2.5 Concentrations in the Beijing–Tianjin–Hebei Urban Agglomeration Using a Geographical and Temporal Weighted Regression Model

Air pollution is the environmental issue of greatest concern in China, especially the PM2.5 pollution in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). Based on sustainable development, it is of interest to study the spatiotemporal distribution of PM2.5 and its influencing mechanisms. This study reveals the temporal evolution and spatial clustering characteristic of PM2.5 pollution from 2015 to 2019, and quantifies the drivers of its natural and socioeconomic factors on it by using a geographical temporal weighted regression model. Results show that PM2.5 concentrations reached their highest level in 2015 before decreasing in the following years. The monthly averages all present a U-shaped change trend. Relative to the traditional high concentrations in the northern part of the BTHUA domain in 2015, the gap in pollution between the north and south has reduced since 2018. The obvious spatial heterogeneity was demonstrated in both the strength and direction of the variables. This study may help identify reasons for high PM2.5 concentrations and suggest appropriate targeted control and prevention measures.