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Abstract Traditional statistical methods (TSM) and machine learning (ML) methods have been widely used to separate the effects of emissions and meteorology on air pollutant concentrations, while their performance compared to the chemistry transport model has been less fully investigated. Using the Community Multiscale Air Quality Model (CMAQ) as a reference, a series of experiments was conducted to comprehensively investigate the performance of TSM (e.g., multiple linear regression and Kolmogorov–Zurbenko filter) and ML (e.g., random forest and extreme gradient boosting) approaches in quantifying the effects of emissions and meteorology on the trends of fine particulate matter (PM2.5) during 2013−2017. Model performance evaluation metrics suggested that the TSM and ML methods can explain the variations of PM2.5 with the highest performance from ML. The trends of PM2.5 showed insignificant differences (p > 0.05) for both the emission-related ( $${{\rm{PM}}}_{2.5}^{{\rm{EMI}}}$$ PM 2.5 EMI ) and meteorology-related components between TSM, ML, and CMAQ modeling results. $${{\rm{PM}}}_{2.5}^{{\rm{EMI}}}$$ PM 2.5 EMI estimated from ML showed the least difference to that from CMAQ. Considering the medium computing resources and low model biases, the ML method is recommended for weather normalization of PM2.5. Sensitivity analysis further suggested that the ML model with optimized hyperparameters and the exclusion of temporal variables in weather normalization can further produce reasonable results in emission-related trends of PM2.5.

Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr−1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr−1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018–2020 compared to that during 2014–2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.

Abstract The result of Aethalometer model to black carbon (BC) source apportionment is highly determined by the absorption Ångström exponent (α) of aerosols from fossil fuel combustion (α ff) and wood burning (α wb). A method using hourly measured potassium to calculate the α ff and α wb values was developed in this study. Results showed that the optimal α ff and α wb were 1.09 and 1.79 for the whole dataset. The optimal α values in the diurnal resolution were also calculated with α ff and α wb varied in 1.02 –1.19 and 1.71–1.90, respectively. Using the dynamic α values, the Pearson correlation coefficient between BC and potassium from wood burning substantially improved compared to the results derived from the fixed α values. The method developed in this study is expected to provide more reasonable BC source identification results, which are helpful for air quality, climate, and human health modeling studies.

Light-absorbing organic carbon (OC), sometimes known as Brown Carbon (BrC), has been recognized as an important fraction of carbonaceous aerosols substantially affecting radiative forcing. This study firstly developed a bottom-up estimate of global primary BrC, and discussed its spatiotemporal distribution and source contributions from 1960 to 2010. The global total primary BrC emission from both natural and anthropogenic sources in 2010 was 7.26 (5.98–8.93 as an interquartile range) Tg, with 43.5% from anthropogenic sources. High primary BrC emissions were in regions such as Africa, South America, South and East Asia with natural sources (wild fires and deforestation) contributing over 70% in the former two regions, while in East Asia, anthropogenic sources, especially residential solid fuel combustion, accounted for over 80% of the regional total BrC emissions. Globally, the historical trend was mainly driven by anthropogenic sources, which increased from 1960 to 1990 and then started to decline. Residential emissions significantly impacted on emissions and temporal trends that varied by region. In South and Southeast Asia, the emissions increased obviously due to population growth and a slow transition from solid fuels to clean modern energies in the residential sector. It is estimated that in primary OC, the global average was about 20% BrC, but this ratio varied from 13% to 47%, depending on sector and region. In areas with high residential solid fuel combustion emissions, the ratio was generally twice the value in other areas. Uncertainties in the work are associated with the concept of BrC and measurement technologies, pointing to the need for more studies on BrC analysis and quantification in both emissions and the air.

Abstract The role of organic aerosols (OA) as ice nucleating particles (INPs) deserves attention because of their high atmospheric abundance. The low concentration of INPs poses challenges in identifying the ice nucleation (IN) of OA among a mix of aerosol types in ambient environment. This study coupled a catalytic stripper system (350°C heating) with a continuous flow diffusion chamber to online investigate the immersion INPs of ambient particles at −30°C at a suburban site. Significant reduction (71 ± 25%) of INP concentrations after evaporation suggested that INPs can be significantly contributed by volatile OA. In addition, nonvolatile OA were more efficient INPs than black carbon. Oxygenated OA by photooxidation and lower ambient promoted the IN activity at noon, when the OA may be more viscous. These results hereby present the first field evidence that OA in anthropogenically influenced regions can be efficient INPs well above the homogeneous IN temperature.

Abstract The less improvement of ambient visibility suspects the government’s efforts on alleviating PM2.5 pollution. The COVID-19 lockdown reduced PM2.5 and increased visibility in Wuhan. Compared to pre-lockdown period, the PM2.5 concentration decreased by 39.0 μg m−3, dominated by NH4NO3 mass reduction (24.8 μg m−3) during lockdown period. The PM2.5 threshold corresponding to visibility of 10 km (PTV10) varied in 54–175 μg m−3 and an hourly PM2.5 of 54 μg m−3 was recommended to prevent haze occurrence. The lockdown measures elevated PTV10 by 9–58 μg m−3 as the decreases in PM2.5 mass scattering efficiency and optical hygroscopicity. The visibility increased by 107%, resulted from NH4NO3 extinction reduction. The NH4NO3 mass reduction weakened its mutual promotion with aerosol water and increased PM2.5 deliquescence humidity. Controlling TNO3 (HNO3 + NO3 −) was more effective to reduce PM2.5 and improve visibility than NH x (NH3 + NH4 +) unless the NH x reduction exceeded 11.7–17.5 μg m−3.

Atmospheric PAHs (polycyclic aromatic hydrocarbons) and their derivatives are a global concern that influences environments and threatens human health. Concentrations of 52 PAHs and the main derivatives in six Chinese megacities were measured in the winter of 2019. The concentrations of ∑PAHs (sum of 52 PAHs) ranged from 19.42 ± 7.68 to 65.40 ± 29.84 ng m−3, with significantly higher levels in northern cities (Harbin [HB], Beijing [BJ], and Xi’an [XA]) than southern ones (Wuhan [WH], Chengdu [CD] and Guangzhou [GZ]). Source apportionment of ∑PAHs was conducted by the PMF model and results showed coal combustion and traffic emissions were the two dominant sources, which dominated ∑PAHs in northern and southern cities, respectively. Biomass burning was also characterized as a crucial source of ∑PAHs and showed extremely high contributions in XA (42.5%). Assisted by the individual PAH source apportionment results, the source-depend TEQ (total BaP equivalent) and incremental lifetime cancer risk (ILCR) were firstly reported in these cities. The results highlighted the contributions of coal combustion and biomass burning to both TEQ and ILCR, which were underestimated by ∑PAHs source apportionment. Secondary organic aerosol-derived PAHs were demonstrated to increase the TEQ compared with the fresh PAHs and three parameters, namely temperature, relative humidity, and O3 concentrations were characterized by multiple linear regression as the principal factors influencing secondary reactions of PAHs in winter. This study provides accurate human health-orientated results and potential control measures to mitigate the toxicity of secondary formed PAHs, and significantly decrease the uncertainty level of traditional methods. The results also revealed great progress in air pollution control by the Chinese government in the past 20 years, but still a long way to go to formulate strict emission control strategies from both environmental and human health-protective perspectives.

Residential coal combustion (RCC) emission exhibited obvious daily variation, while no real-time estimation of air pollutants from RCC has been reported, as the shortages of corresponding activity dataset and emission factors with high time resolution. A real-time monitoring platform for RCC emission was established. Hourly emission factors of 18 typed of TEs from eleven kinds of chunk coals and nine kinds of honeycomb coals burning in China were obtained. The monthly and hourly coal consumption amounts were calculated with reference and our field survey. Then the hourly TEs emission inventories from RCC were established in China. GEOS-Chem and Risk Quotients Models were utilized to map the spatialized health risks of hazardous elements, including the gridded hazard index and carcinogenic risk. The result indicated that the EFs of TEs would be underestimated if the tests only consider flaming conditions. Cu, K, Ca, Zn, and Co were the top five elements from RCC, with corresponding emission amounts as 1397.7, 1054.0, 676.0, 623.5 and 420 tons in 2017, respectively. K, Ti, Fe, Sn, and Sb showed hourly peak values under flaming dominated periods, accounting for 48.2%, 45.9%, 31.8%, 42.8%, and 33.8% of their daily emissions. Other elements (e.g., V, Co, As, Hg and Pb) exhibited higher emissions under smoldering dominated period in nighttime, accounting for 22.2%, 32.9%, 27.6%, 34.7%, and 28.4% of their daily emissions. TEs emission from RCC closely follows the habits of human daily cooking and heating activity. The national HI were lower than the acceptable level (HI ≤ 1) except Sichuan Province (up to 1.2). Higher carcinogenic risks (≥1 × 10-6) occurred in parts of Sichuan, Shanxi, Hunan and Hubei, which were up to 2.0 × 10-5. The high-resolution TEs emission inventories could be useful for future modeling works on the formation and evolution of air pollution and are helpful for human exposure assessment.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality.As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Biomass burning (BB) has significant impacts on air quality, climate and human health. In China, the BB emission has changed substantially over the past decades while the multi-year variation held high uncertainty and the driving forces have addressed little attention. Here, this research aimed to conduct a comprehensive and systematic analysis of BB variation in China and provided precise and targeted BB emission reduction suggestions. The moving of high emission for BB from 2003 to 2014 was clearly identified, by the view of reliable emission estimation and anthropogenic impacts. Multiple satellite products, field survey, time varying biomass loading data and measured emission factors were adopted to better estimating BB emission and reducing the uncertainty. Social-economic analysis was added to assess the anthropogenic impacts on high emission variation quantitatively. Results showed that the cumulative BB emissions of OC, EC, CH4, NOX, NMVOC, SO2, NH3, CO, CO2, PM2.5 and PM10 during 2003-2014 were 1.6 × 104, 5.64 × 103, 3.57 × 104, 1.7 × 104, 5.44 × 104, 2.96 × 103, 6.77 × 103, 6.5 × 105, 1.15 × 107, 5.26 × 104 and 6.04 × 104 Gg, respectively. Crop straw burning (in-field and domestic) in northeast China plain (NEP), north China plain (NCP), northern arid and semiarid region and loess plateau were the key sources, averagely contributed 73% for all the pollutants emission. While domestic straw burning and firewood burning in Sichuan basin (SB), Yunnan-Guizhou plateau and southern China were main contributors, averagely accounting for 70% of all the pollutants emission. On regional level, high emissions were mainly found in SB, NCP and NEP. Temporally, high emissions were mainly found in crop sowing harvesting and heating seasons. From 2003 to 2014, the BB emission for different biomass species has changed significantly in different regions. High emission has gradually moved from SB to NCP and NEP. Firewood burning and domestic straw burning emission decreased by 47% and 14% in SB, respectively. In-field straw burning emission increased by 52% and 231% in NCP and NEP respectively and domestic straw burning emission increased by 62% in NEP. Emissions from heating season have decreased while emissions in corn harvest season were continuously increased. Analysis of Environmental kuznets curve, agricultural productivity level, human burning habits, rural energy structure and local control policies revealed the internal human driving strength of the variation for BB emission. The unbalanced development of social economy and the policy bias were primary drivers of limiting the BB management. BB emission will alleviate in NCP and aggravate in NEP. For the further emission reduction, effective measures for corn sources management, straw returning and rural energy utilization should be systematically considered. This research provides a clear evidence for the multi-year variation pattern of BB emissions, which is critical for pollution prediction, air quality modeling and targeted mitigation strategies for the key regions of China.

The extent of PM2.5 pollution has reduced in traditional polluted regions such as the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin (SB), and Pearl River Delta (PRD) over China in recent years. Despite this, the Twain-Hu Basin (THB), which covers the lower flatlands in Hubei and Hunan provinces in central China, was found to be a high PM2.5 pollution region, with annual mean PM2.5 concentrations of 41–63 μg·m−3, which is larger than the values in YRD, SB, and PRD during 2014–2019, and high aerosol optical depth values (>0.8) averaged over 2000–2019 from the MODIS products. Heavy pollution events (HPEs) are frequently observed in the THB, with HPE-averaged concentrations of PM2.5 reaching up to 183–191 μg·m−3, which exceeds their counterparts in YRD, SB, and PRD for 2014–2019, highlighting the THB as a center of heavy PM2.5 pollution in central China. During 2014–2019, approximately 65.2% of the total regional HPEs over the THB were triggered by the regional transport of PM2.5 over Central and Eastern China (CEC). This occurred in view of the co-existing HPEs in the NCP and the THB, with a lag of almost two days in the THB-PM2.5 peak, which is governed by the strong northerlies of the East Asian monsoon (EAM) over CEC. Such PM2.5 transport from upstream source regions in CEC contributes 60.3% of the surface PM2.5 pollution over the THB receptor region. Hence, a key PM2.5 receptor of the THB in regional pollutant transport alters the distribution patterns of PM2.5 pollution over China, which is attributable to the climate change of EAMs. This study indicates a complex relationship between sources and receptors of atmospheric aerosols for air quality applications.

Abstract Morphology, composition, and mixing state of individual particles emitted from crop residue, wood, and solid waste combustion in a residential stove were analyzed using transmission electron microscopy (TEM). Our study showed that particles from crop residue and apple wood combustion were mainly organic matter (OM) in smoldering phase, whereas soot-OM internally mixed with K in flaming phase. Wild grass combustion in flaming phase released some Cl-rich-OM/soot particles and cardboard combustion released OM and S-rich particles. Interestingly, particles from hardwood (pear wood and bamboo) and softwood (cypress and pine wood) combustion were mainly soot and OM in the flaming phase, respectively. The combustion of foam boxes, rubber tires, and plastic bottles/bags in the flaming phase released large amounts of soot internally mixed with a small amount of OM, whereas the combustion of printed circuit boards and copper-core cables emitted large amounts of OM with Br-rich inclusions. In addition, the printed circuit board combustion released toxic metals containing Pb, Zn, Sn, and Sb. The results are important to document properties of primary particles from combustion sources, which can be used to trace the sources of ambient particles and to know their potential impacts in human health and radiative forcing in the air.

In 2020, when the novel coronavirus disease 2019 (COVID-19) broke out as a global pandemic, cities in Hubei Province first went into lockdown on 23 January and resumed work and production on 20 March. From February to March 2020, human activities in Hubei decreased significantly, with the average particulate matter smaller than 2.5 μm (PM2.5) concentration standing at 40 μg/m3, which is 21% lower than the expected based on a linear fitting trend in thePM2.5 concentration in Hubei. By using the empirical orthogonal function (EOF) method, this paper comparatively analyzes the spatial-temporal variations of Hubei’s PM2.5 concentration anomaly in February and March 2020 and the same periods of 2016–2019. The results show that the daytime peak of the PM2.5 daily variation in Hubei in a low-emission scenario during COVID-19 declined significantly, to which human activities contributed the most. However, during nighttime, the PM2.5 peak became more prominent, and the meteorological conditions had a more noticeable effect on the PM2.5 concentration. In addition, during COVID-19, there was a great drop in PM2.5 pollution accumulated from local sources within the urban circle of Wuhan City, while an increase was seen in central-western Hubei due to the inter-regional pollutant transport. Thus, the high PM2.5 concentration center in the urban circle of Wuhan disappeared, but the pollution transport channel cities in central-western Hubei remained as high-PM2.5-concentration centers.

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network (FWNN) including the neural network (NN), the fuzzy logic (FL), the wavelet transform (WT), and the genetic algorithm (GA) was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network (FNN), the wavelet neural network (WNN), and the neural network (ANN). The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river.

The fast development of a secondary aerosol layer was observed over megacities in eastern Asia during summertime. Within three hours, from midday to early afternoon, the contribution of secondary aerosols above the planetary boundary layer (PBL) increased by a factor of three to five, and the coatings on black carbon (BC) also increased and enhanced its absorption efficiency by 50%. This tended to result from the intensive actinic flux received above the PBL which promoted photochemical reactions. The absorption of BC could be further amplified by the strong reflection of solar radiation over the cloud top across the PBL. This enhanced heating effect of BC introduced by combined processes (intensive solar radiation, secondary formation and cloud reflection) may considerably increase the temperature inversion above the PBL. This mechanism should be considered when evaluating the radiative impact of BC, especially for polluted regions receiving strong solar radiation.