Long-term (2006-2015) variations and relations of multiple atmospheric pollutants based on multi-remote sensing data over the North China Plain

Long-Term Air Pollution Characteristics and Multi-scale Meteorological Factor Variability Analysis of Mega-mountain Cities in the Chengdu-Chongqing Economic Circle

Currently, air quality has become central to global environmental policymaking. As a typical mountain megacity in the Cheng-Yu region, the air pollution in Chongqing is unique and sensitive. This study aims to comprehensively investigate the long-term annual, seasonal, and monthly variation characteristics of six major pollutants and seven meteorological parameters. The emission distribution of major pollutants is also discussed. The relationship between pollutants and the multi-scale meteorological conditions was explored. The results indicate that particulate matter (PM), SO2 and NO2 showed a "U-shaped" variation, while O3 showed an "inverted U-shaped" seasonal variation. Industrial emissions accounted for 81.84%, 58% and 80.10% of the total SO2, NOx and dust pollution emissions, respectively. The correlation between PM2.5 and PM10 was strong (R = 0.98). In addition, PM only showed a significant negative correlation with O3. On the contrary, PM showed a significant positive correlation with other gaseous pollutants (SO2, NO2, CO). O3 is only negatively correlated with relative humidity and atmospheric pressure. These findings provide an accurate and effective countermeasure for the coordinated management of air pollution in Cheng-Yu region and the formulation of the regional carbon peaking roadmap. Furthermore, it can improve the prediction accuracy of air pollution under multi-scale meteorological factors, promote effective emission reduction paths and policies in the region, and provide references for related epidemiological research.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11270-023-06279-8.

Assessment of air quality in North Korea from satellite observations

North Korea's air quality is poorly understood due to a lack of reliable data. Here, we analyzed urban- to national-scale air quality changes in North Korea using multi-year satellite observations. Pyongyang, Nampo, Pukchang, and Munchon were identified as pollution hotspots. On a national scale, we found that North Korea experienced 6.7, 17.8, and 20.6 times greater amounts of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO) per unit primary energy supply (PES) than South Korea from 2005 to 2018. Besides, North Korea had a 24.3 times larger aerosol optical depth (AOD) per PES than South Korea from 2011 to 2018. Severe CO and aerosol pollution is aligned with extensive biofuel combustion. High SO₂ pollution corresponds with the strong coal dependence of the industry. The change rates of the national average columns for NO₂, SO₂, and CO were + 3.6, -4.4, and -0.4 % yr^-1, respectively. The AOD change rate was -4.8 % yr^-1. Overall decreasing trends, except for NO₂, are likely due to a decline in coal-fired PES. Positive NO₂ trends are consistent with increasing industrial activities. Each pollutant showed consistent patterns of linear trends, even after correcting the influence of transboundary pollution. Flue gas control and biofuel consumption reduction seem necessary to improve North Korea's air quality.

Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019

The study analyzed the current status and changing trends of PM_2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM_2.5 pollution during 2015-2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China's annual grade II air quality standard (35 μg/m³). The spatial distribution of PM_2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM_2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.

Comparison of PM2.5 and CO2 Concentrations in Large Cities of China during the COVID-19 Lockdown

Estimating the impacts on PM_2.5 pollution and CO₂ emissions by human activities in different urban regions is important for developing efficient policies. In early 2020, China implemented a lockdown policy to contain the spread of COVID-19, resulting in a significant reduction of human activities. This event presents a convenient opportunity to study the impact of human activities in the transportation and industrial sectors on air pollution. Here, we investigate the variations in air quality attributed to the COVID-19 lockdown policy in the megacities of China by combining in-situ environmental and meteorological datasets, the Suomi-NPP/VIIRS and the CO₂ emissions from the Carbon Monitor project. Our study shows that PM_2.5 concentrations in the spring of 2020 decreased by 41.87% in the Yangtze River Delta (YRD) and 43.30% in the Pearl River Delta (PRD), respectively, owing to the significant shutdown of traffic and manufacturing industries. However, PM_2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region only decreased by 2.01% because the energy and steel industries were not fully paused. In addition, unfavorable weather conditions contributed to further increases in the PM_2.5 concentration. Furthermore, CO₂ concentrations were not significantly affected in China during the short-term emission reduction, despite a 19.52% reduction in CO₂ emissions compared to the same period in 2019. Our results suggest that concerted efforts from different emission sectors and effective long-term emission reduction strategies are necessary to control air pollution and CO₂ emissions.

Identification of NOx hotspots from oversampled TROPOMI NO2 column based on image segmentation method

Satellite-based measures of NO₂ have become increasingly available for resolving the limitation on insufficient spatial and temporal coverage of ground-level monitoring networks. Oversampled NO₂ column density can obtain more detailed features of NO₂ column with a spatial resolution as high as 2 km × 2 km, while it is still challenging to identify hotspots of NO_x pollution plume in city-scale due to background interference. In this study, we proposed a method for detecting the NO_x hotspot grids from oversampled satellite NO₂ column based on the image segmentation method, and identifying major source types using Term frequency-inverse document frequency (TF-IDF). A fractal model was used to evaluate and eliminate the background portion of the NO₂ column and an adaptive threshold method was adopted to identify the region of interest (ROI) of local hotspot NO₂ column. Hot-grid index, counting the frequency of NO₂ hotspot ROI in each grid, was conducted to identify the hotspot grids. TF-IDF was used to semantically analyze the major source types of NO₂ hotspot grids. Taking Central and Eastern China as the studied domain, the hotspot grids of NO₂ and the relevant major source types were identified based on the proposed method. The major non-road mobile sources (such as Beijing Capital International Airport), industrial areas (such as Caofeidian Industrial Park) and urban areas were clearly distinguished. The power plant, Coke and Iron and Steel were identified as major source types in the whole year in the corresponding NO_x hotspot grids. Notably, the identification of hotspot grids indicated a higher probability of a local high-intensity NO_x pollution plume rather than a quantitative NO_x emission; the key source types were the semantic keywords in hotspot grids, which does not mean there were no other exiting emission sources. This proposed method has strong implications on rapidly identifying the NO_x hotspot grids based on oversampled TROPOMI NO₂ column and the list of industrial enterprises.

Prenatal exposure to air pollution and the risk of preterm birth in rural population of Henan Province

Due to the poor living and healthcare conditions, preterm birth (PTB) in rural population is a pressing health issue. However, PTB studies in rural population are rare. To explore the effects of air pollutants on PTB in rural population, we collected 697,316 medical records during 2014-2016 based on the National Free Preconception Health Examination Project. Logistic regression models were used to estimate the association between air pollutants and PTB and the modifying effects of demographic characteristics. Relative contribution and principal component analysis-generalized linear model (PCA-GLM) analysis were used to explore the most significant air pollutant and gestational period. Our results demonstrated that PTB risk is positively associated with exposure to air pollutants including PM₁₀, PM_2.5, SO₂, NO₂, and CO, while negatively associated with O₃ exposure (P < 0.05). In addition, we found that NO₂ was the largest contributor to the risk of PTB caused by air pollutants (26.5%). The third trimester of pregnancy was the most sensitive exposure window. PCA-GLM analysis showed that the first component (a combination of PM, SO₂, NO₂, and CO) increased the risk of PTB. Moreover, we found that rural women who are younger, had higher educated, multi-parity, or smoke appeared to be more sensitive to the association between air pollutants exposure and PTB (P_-interaction<0.05). Our findings suggested that increased air pollutants except O₃ were associated with elevated PTB risk, especially among vulnerable mothers. Therefore, the effects of air pollutants exposure on PTB should be mitigated by restricting emission sources of NO₂ and SO₂ in rural population, especially during the third trimester.

Spatio-Temporal Characteristics of SO2 across Weifang from 2008 to 2020

China has achieved good results in SO₂ pollution control, but SO₂ pollution still exists in some areas. Analyzing the spatio-temporal distribution of SO₂ is critical for regional SO₂ pollution prevention and control. Compared with existing air pollution studies that paid more attention to PM₂.₅, NO₂ and O₃, and focused on the macro scale, this study took the small-scale Weifang city as the research area, analyzed the temporal and spatial changes in SO₂, discussed the migration trajectory of SO₂ pollution and explored the impact of wind on SO₂ pollution. The results show that the average annual concentration of SO₂ in Weifang has exhibited a downward trend in the past 13 years, showing the basic characteristics of “highest in winter, lowest in summer and slightly higher in spring and autumn”, “highest on Sunday, lowest on Thursday and gradually decreasing from Monday to Thursday” and “highest at 9 a.m., lowest at 4 p.m. and gradually increasing from midnight to 9 a.m.”. SO₂ concentration showed obvious spatial heterogeneity: higher in the north and lower in the south. In addition, Shouguang, Changyi and Gaomi were seriously polluted. The SO₂ pollution shifted from south to northeast. The clean wind direction (southeast wind and northeast wind) of Weifang city accounted for about 41%, and the pollution wind direction (northwest wind and west wind) accounted for about 7%. Drawing from the multi-scale analysis, vegetation, precipitation, temperature, transport situation and human activity were the most relevant factors. Limited to data collection, more quantitative research is needed to gain insight into the influence mechanism in the future.

NOx Emission from Diesel Vehicle with SCR System Failure Characterized Using Portable Emissions Measurement Systems

Nitrogen oxides (NOx) emissions from diesel vehicles are major contributors to increasing fine particulate matter and ozone levels in China. The selective catalytic reduction (SCR) system can effectively reduce NOx emissions from diesel vehicles and is widely used in China IV and V heavy-duty diesel vehicles (HDDVs). In this study, two China IV HDDVs, one with SCR system failure and the other with a normal SCR system, were tested by using a portable emissions measurement system (PEMS). Results showed that the NOx emission factors of the test vehicle with SCR system failure were 8.42 g/kW∙h, 6.15 g/kW∙h, and 6.26 g/kW∙h at loads of 0%, 50%, and 75%, respectively, which were 2.14, 2.10, and 2.47 times higher than those of normal SCR vehicles. Emission factors, in terms of g/km and g/kW∙h, from two tested vehicles were higher on urban roads than those on suburban and motorways. The NOx emission factor of the vehicle with failed SCR system did not meet the China IV emission standard. The time-weighted results for normal SCR vehicle over the three road types show that, except for NOx emission factor 12.17% higher than the China IV limit at 0% load, the emission values are 16.21% and 27.54% below the China IV standard limit at 50% load and 75% load, respectively. In general, with higher load, NOx emissions (in terms of g/kW∙h) from the tested vehicle decreased. Furthermore, NO/NOx concentrations of both vehicles with normal and failed SCR systems showed a decreasing trend with the increase in load.

The effect of air pollution on immunological, antioxidative and hematological parameters, and body condition of Eurasian tree sparrows

Air pollution constitutes potential threats to wildlife and human health; therefore, it must be monitored accurately. However, little attention has been given to understanding the toxicological effects induced by air pollution and the suitability of bird species as bioindicators. The Eurasian tree sparrow (Passer montanus), a human commensal species, was used as a study model to examine toxic metal accumulation, retention of particulate matter (PM), immunological and antioxidant capacities, and hematological parameters in birds inhabiting those areas with relatively higher (Shijiazhuang city) or lower (Chengde city) levels of PM_2.5 and PM₁₀ in China. Our results showed that Shijiazhuang birds had significantly more particle retention in the lungs and toxic metal (including aluminum, arsenic, cadmium, iron, manganese, and lead) accumulation in the feathers relative to Chengde birds. They also had lower superoxide dismutase, albumin, immunoglobulin M concentrations in the lung lavage fluid, and total antioxidant capacity (T-AOC) in the lungs and hearts. Furthermore, although they had higher proportions of microcytes, hypochromia, and polychromatic erythrocytes in the peripheral blood (a symptom of anemia), both populations exhibited comparable body conditions, white cell counts, heterophil and lymphocyte ratios, and plasma T-AOC and corticosterone levels. Therefore, our results not only confirmed that Shijiazhuang birds experienced a greater burden from environmental PM and toxic metals but also identified a suite of adverse effects of environmental pollution on immunological, antioxidative, and hematological parameters in multiple tissues. These findings contribute to our understanding of the physiological health consequences induced by PM exposure in wild animals. They suggest that free-living birds inhabiting urban areas could be used as bioindicators for evaluating the adverse effects induced by environmental pollution.

Development of a high-resolution emission inventory of agricultural machinery with a novel methodology: A case study for Yangtze River Delta region

Recent increased use of agricultural machines elevated the atmospheric pollutant emissions in the Yangtze River Delta (YRD) region in eastern China. Given the potentially large environmental and health impacts in busy seasons with enhanced machinery usage, it is important to accurately estimate the magnitude, spatial and temporal distributions of the emissions. We developed a novel method to estimate the real-world in-use agricultural machinery emissions, by combining satellite data, land and soil information, and in-house investigation. The machinery usage was determined based on the spatial distribution, growing and rotation pattern of the crops. The varied requirement of machinery power by heterogeneous soil texture, which was ignored in the previous studies, was considered in our methodology. The spatiotemporal pattern of machinery usage was determined based on the explored quantitative correlation between the local agricultural activity duration and the geographic location of the activity. A "grid-based" (30 × 30 m) inventory with daily emissions was then obtained, achieving significant improvement on spatial and temporal resolution. It substantially diminished the bias of previous inventories based on the machinery population or power installation census data. The emissions of NO_X, PM_2.5, CO and THC were estimated at 36300, 2000, 36900 and 8430 metric tons in YRD, with the majority contribution from Anhui and Jiangsu. Ten cities locating in northern and central Anhui and Jiangsu contributed the largest machinery emissions, accounting for 60% of the total emissions in YRD. Harvesting was found to have the largest emissions, followed by tilling and planting. Regarding the crops, the emissions from wheat and rice related machinery usage were the largest. In the busy seasons (spring and autumn), larger daily NO_X and PM_2.5 emissions were found from machinery than on-road vehicles in 42% of counties in Anhui and Jiangsu, highlighting the necessity of careful strategy making on controls of priority emission source.

The spatially heterogeneous response of aerosol properties to anthropogenic activities and meteorology changes in China during 1980-2018 based on the singular value decomposition method

The unsustainable and rapid economy development brings air pollution prominently in China. In the last decade, the haze weather and its influencing mechanism across China have received increasingly attention. Although previous research has extensively focused on the characteristics of aerosols, better understanding of long-term variation in aerosols and their determinants since the Reform and Opening-up still lack in China. Furthermore, the previous studies exploring the influencing mechanism behind haze episodes by using statistical method only reflect correlation between pollutant concentration and indicators at single station, which cannot consider the remote influences resulting from atmosphere transport. In this research, we investigated the spatiotemporal pattern of aerosol optical depth (AOD) and aerosol species in China during 1980-2018 and explored the spatially heterogeneous response of AOD and aerosol component to meteorological conditions and urbanization based on singular value decomposition (SVD) method. The results indicated that AOD exhibited an upward trend in nearly 40 years, especially in eastern China with the fastest growth of sulfate aerosol. The heterogeneity of determinants revealed a great gap in anthropogenic activities and meteorological influences on aerosol varing regions. In eastern China, anthropogenic activities should be closely monitored. Besides, scientific desert governance and urban construction exert positive impact on air pollution in Xinjiang province.

Dual-modelling-based source apportionment of NOx in five Chinese megacities: Providing the isotopic footprint from 2013 to 2014

In China, nitrate (NO₃^-) becomes the main contributor to fine particles (PM_2.5) because the emissions of its precursor, nitrogen oxides (NO_x), were not recognized and controlled well in recent years. In this work, sources, conversion, and geographical origin of NO_x were interpreted combining the isotopic information (δ¹⁵N and δ¹⁸O) of NO₃^- and dual modelling at five Chinese megacities (Beijing, Shanghai, Guangzhou, Wuhan and Chengdu) during 2013-2014. Results showed that the δ¹⁵N-NO₃^- values (n = 512) ranged from -12.3‰ to +22.9‰, and the average δ¹⁸O-NO₃^- value was +83.4‰ ± 17.2‰. The isotopic compositions both had a rising tendency as ambient temperature dropped, attributing largely to the source changes. Bayesian model indicated the percentage for the OH pathway of NO_x conversion had a clear seasonal variation with a higher value during summer (58.0% ± 9.82%) and a lower value during winter (11.1% ± 3.99%); it was also significantly correlated with latitude (p < 0.01). Coal combustion was the most important source of NO_x (31.1%-41.0%), which was geographically derived from North China and other south-central developed regions implied by Potential Source Contribution Function (PSCF). Apart from Chengdu, mobile sources was the second largest contributor to NO_x. This source was extensive but uniformly distributed all around the typical urban agglomerations of China. Biomass burning and microbial processes shared similar source areas, mostly originating from the North China Plain and Sichuan Basin. Based on the NO_x features, we infer that residential coal combustion was the primary source of heavy PM_2.5 pollution in Chinese megacities. Controlling the source categories of these regional priorities would help mitigate atmospheric pollution in these areas.

Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations

The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO2), and nitrogen dioxide (NO2) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO2 of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO2 grew from ~3.2 to 5.9 × 1015 molecules cm−2 during 2005–2011 and deceased to ~3.9 × 1015 molecules cm−2 in 2017. Unlike the steadily declining SO2, variations of OMI NO2 flattened out in 2016 and increased ~0.5 × 1015 molecules cm−2 during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO2 has a high correlation (R > 0.8), indicating that annual variations of SO2 can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O3 and NOx, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O3) and NO2 since 2016 suggests that more control measures should be made to reduce O3-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.