Causative impact of air pollution on evapotranspiration in the North China Plain

Air pollution and climate change as grand challenges to sustainability

There is a cross-disciplinary link between air pollution, climate crisis, and sustainable lifestyle as they are the most complex struggles of the present century. This review takes an in-depth look at this relationship, considering carbon dioxide emissions primarily from the burning of fossil fuels as the main contributor to global warming and focusing on primary SLCPs such as methane and ground-level ozone. Such pollutants severely alter the climate through the generation of greenhouse gases. The discussion is extensive and includes best practices from conventional pollution control technologies to hi-tech alternatives, including electric vehicles, the use of renewables, and green decentralized solutions. It also addresses policy matters, such as imposing stricter emissions standards, setting stronger environmental regulations, and rethinking some economic measures. Besides that, new developments such as congestion charges, air ionization, solar-assisted cleaning systems, and photocatalytic materials are among the products discussed. These strategies differ in relation to the local conditions and therefore exhibit a varying effectiveness level, but they remain evident as a tool of pollution deterrence. This stresses the importance of holistic and inclusive approach in terms of engineering, policies, stakeholders, and ecological spheres to tackle.

Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors

Despite the ecological and socio-economic importance of Eurasian steppe, the land use/cover change, land degradation and the threats facing this precious ecosystem still have not been comprehensively understood. Taking advantages of the land use/cover change monitoring platform (FROM-GLC Plus), this study developed the annual land use/cover maps during 2000-2022, and the land use/cover change, especially the change of grassland, was further analyzed. The grassland area exhibited a net increase, predominantly transformed from cropland, forest, and bareland, accounting for 17.64 %, 31.91 %, and 45.60 %, respectively. To monitor land degradation, we adopted the framework suggested by the United Nations Convention to Combat Desertification (UNCCD). According to the monitoring result, grassland constituted the highest proportion of degraded land (39.82 %). This may due to its dominance in the Eurasian steppe's land use/cover, as the extent of grassland degradation (1.92 %) was lower than the overall land degradation level (2.83 %) across the region. To offer tailored and sustainable development recommendations, we quantified the driving factors behind land dynamics using the geographical detector model and convergent cross mapping (CCM), considering both spatial and temporal dimensions. Environmental and socio-economic factors, such as precipitation, temperature, urbanization, mining and grazing intensity, etc., were integrated into the analysis. We found that urbanization, cropland and moisture distribution emerged as key drivers influencing land degradation's spatial distribution in the Eurasian steppe, while temperature variations between years impacted vegetation changes. This research thus provides a deeper understanding of the region's land dynamics, enhancing comprehensive monitoring of the Eurasian steppe's land dynamics. Moreover, it serves as a foundation for policymakers and land managers to devise conservation strategies and sustainable development initiatives for this critical ecosystem.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

An enhanced loss function in deep learning model to predict PM2.5 in India

Fine particulate matter (PM2.5) is one of the major air pollutants and is an important parameter for measuring air quality levels. High concentrations of PM2.5 show its impact on human health, the environment, and climate change. An accurate prediction of fine particulate matter (PM2.5) is significant to air pollution detection, environmental management, human health, and social development. The primary approach is to boost the forecast performance by reducing the error in the deep learning model. So, there is a need to propose an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM2.5 concentrations. This paper proposes the ELF in CTLSTM (Chi-Square test Long Short Term Memory) to improve the PM2.5 forecast. The ELF in the CTLSTM model gives more accurate results than the standard forecast models and other state-of-the-art deep learning techniques. The proposed ELFCTLSTM reduces the prediction error of by a maximum of 10 to 25 percent than the state-of-the-art deep learning models.

Differences in Reference Evapotranspiration Variation and Climate-Driven Patterns in Different Altitudes of the Qinghai–Tibet Plateau (1961–2017)

Reference evapotranspiration (ET0) in the hydrological cycle is one of the processes that is significantly affected by climate change. The Qinghai–Tibet Plateau (QTP) is universally recognized as a region that is sensitive to climate change. In this study, an area elevation curve is used to divide the study area into three elevation zones: low (below 2800 m), medium (2800–3800 m) and high (3800–5000 m). The cumulative anomaly curve, Mann–Kendall test, moving t-test and Yamamoto test results show that a descending mutation occurred in the 1980s, and an ascending mutation occurred in 2005. Moreover, a delay effect on the descending mutation in addition to an enhancement effect on the ascending mutation of the annual ET0 were coincident with the increasing altitude below 5000 m. The annual ET0 series for the QTP and different elevation zones showed an increasing trend from 1961 to 2017 and increased more significantly with the increase in elevation. Path analysis showed that the climate-driven patterns in different elevation zones are quite different. However, after the ascending mutations occurred in 2005, the maximum air temperature (Tmax) became the common dominant driving factor for the whole region and the three elevation zones.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

Chemical Characteristics and Sources of Submicron Particles in a City with Heavy Pollution in China

Submicron particle (PM1) pollution has received increased attention in recent years; however, few studies have focused on such pollution in the city of Shijiazhuang (SJZ), which is one of the most polluted cities in the world. In this study, we conducted an intensive simultaneous sampling of PM1 and PM2.5 in autumn 2016, in order to explore pollution characteristics and sources in SJZ. The results showed that the average mass concentrations of PM1 and PM2.5 were 70.51 μg/m3 and 91.68 μg/m3, respectively, and the average ratio of PM1/PM2.5 was 0.75. Secondary inorganic aerosol (SIA) was the dominant component in PM1 (35.9%) and PM2.5 (32.3%). An analysis of haze episodes found that SIA had a significant influence on PM1 pollution, NH4+ promoted the formation of pollution, and SO42− and NO3− presented different chemical mechanisms. Additionally, the results of source apportionment implied that secondary source, biomass burning and coal combustion, traffic, industry, and dust were the major pollution sources for SJZ, accounting for 45.4%, 18.9%, 15.7%, 10.3%, and 9.8% of PM1, respectively, and for 42.4%, 18.8%, 12.2%, 10.2%, and 16.4% of PM2.5, respectively. Southern Hebei, mid-eastern Shanxi, and northern Henan were the major contribution regions during the study period. Three transport pathways of pollutants were put forward, including airflows from Shanxi with secondary source, airflows from the central Beijng–Tianjin–Hebei region with fossil fuel burning source, and airflows from the southern North China Plain with biomass burning source. The systematic analysis of PM1 could provide scientific support for the creation of an air pollution mitigation policy in SJZ and similar regions.

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Analyzing the association between fine particulate matter (PM_2.5) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m³ in mainland China, and the highest population exposure to PM_2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM_2.5 was positively related to GDP. An apparent upward trend of population exposure to PM_2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM_2.5 increases by 1.05 person/km² at the 80th percentile, and 1.88 person/km² at the 95th percentile, respectively.

A Review of Recent Advances in Research on PM2.5 in China

PM_2.5 pollution has become a severe problem in China due to rapid industrialization and high energy consumption. It can cause increases in the incidence of various respiratory diseases and resident mortality rates, as well as increase in the energy consumption in heating, ventilation, and air conditioning (HVAC) systems due to the need for air purification. This paper reviews and studies the sources of indoor and outdoor PM_2.5, the impact of PM_2.5 pollution on atmospheric visibility, occupational health, and occupants’ behaviors. This paper also presents current pollution status in China, the relationship between indoor and outdoor PM_2.5, and control of indoor PM_2.5, and finally presents analysis and suggestions for future research.