Characteristics of PM2.5 bounded carbonaceous aerosols, carbon dioxide and its stable carbon isotopes (δ13C) in rural households in northwest China: Effect of different fuel combustion.

In order to fully understand the carbon emission from different fuels in rural villages of China, especially in the typical atmospheric pollution areas. The characteristics of carbonaceous aerosols and carbon dioxide (CO2) with its stable carbon isotope (δ13C) were investigated in six households, which two households used coal, two households used wood as well as two households used biogas and liquefied petroleum gas (LPG), from two rural villages in Fenwei Plain from March to April 2021. It showed that the fine particulate matter (PM2.5) emitted from biogas and LPG couldn't be as lower as expected in this area. However, the clean fuels could relatively reduce the emissions of organic carbon (OC) and element carbon (EC) in PM2.5 compare to the solid fuels. The pyrolyzed carbon (OP) accounted more total carbon (TC) in coal than the other fuels use households, indicating that more water-soluble OC existed, and it still had the highest secondary organic carbon (SOC) than the other fuels. Meantime, the coal combustions in the two villages had the highest CO2 concentration of 527.6 ppm and 1120.6 ppm, respectively, while the clean fuels could effectively reduce it. The average δ13C values (-26.9‰) was much lighter than almost all the outdoor monitoring and similar to the δ13C values for coal combustion and vehicle emission, showing that they might be the main contributors of the regional atmospheric aerosol in this area. During the sandstorm, the indoor PM2.5 mass and CO2 were increasing obviously. The indoor cancer risk of PAHs for adults and children were greater than 1 × 10-6, exert a potential carcinogenic risk to human of solid fuels combustion in rural northern China. It is important to continue concern the solid fuel combustion and its health impact in rural areas.

Historical trajectories of antibiotics resistance genes assessed through sedimentary DNA analysis of a subtropical eutrophic lake.

Investigating the occurrence of antibiotic-resistance genes (ARGs) in sedimentary archives provides opportunities for reconstructing the distribution and dissemination of historical (i.e., non-anthropogenic origin) ARGs. Although ARGs in freshwater environments have attracted great attention, historical variations in the diversity and abundance of ARGs over centuries to millennia remain largely unknown. In this study, we investigated the vertical change patterns of bacterial communities, ARGs and mobile genetic elements (MGEs) found in sediments of Lake Chenghai spanning the past 600 years. Within resistome preserved in sediments, 177 ARGs subtypes were found with aminoglycosides and multidrug resistance being the most abundant. The ARG abundance in the upper sediment layers (equivalent to the post-antibiotic era since the 1940s) was lower than those during the pre-antibiotic era, whereas the ARG diversity was higher during the post-antibiotic era, possibly because human-induced lake eutrophication over the recent decades facilitated the spread and proliferation of drug-resistant bacteria. Statistical analysis suggested that MGEs abundance and the bacterial community structure were significantly correlated with the abundance and diversity of ARGs, suggesting that the occurrence and distribution of ARGs may be transferred between different bacteria by MGEs. Our results provide new perspectives on the natural history of ARGs in freshwater environments and are essential for understanding the temporal dynamics and dissemination of ARGs.

Correction: Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements.

[This corrects the article DOI: 10.1371/journal.pone.0287187.].

Reconstructing atmospheric 129I deposition over 170 years with the varved sediment in the Sihailongwan Maar Lake, northeast China.

Long-term deposition of atmospheric radioactive iodine-129 (129I) is important for assessing the impact of human nuclear activities (HNAs), but still not well understood in East Asia. In this study, we quantitatively reconstructed the deposition history of airborne 129I using varved sediment from Sihailongwan Maar Lake (SHLW) in northeast China. Our results revealed significant increases in 129I concentrations and 129I/127I atomic ratios since the 1950s, indicating the influence of HNAs on the environment and marking the onset of the Anthropocene. The variation of 129I in the investigated site can be primarily attributed to the global fallout of ANWT as well as nuclear fuel reprocessing in Europe, Russia and the USA. Notably, neither the Chernobyl nor the Fukushima nuclear accidents have had any discernable impact on the SHLW Lake. Over the past 170 years (1846-2021), the reconstructed fluxes indicate a rapid increase in 129I deposition from the early 1950s until the 1970s followed by dramatic changes thereafter. The measured 129I fluxes range between (1.26-349) × 109 atoms m-2 yr-1 in the SHLW Lake, which are consistent with similar latitude zones across East Asia, but differ significantly from those observed in high-elevation glaciers within the Northern Hemisphere due to prevailing atmospheric circulation patterns. The total 129I inventory was calculated to be 11.9 × 1012 atoms m-2, with natural and anthropogenic 129I accounting for 2.86 % and 97.1 %, respectively, suggesting an overwhelming artificial contribution. The reconstructed fluxes and inventory of atmospheric 129I deposition quantitatively distinguish the natural and artificial contributions, and provide a novel insight into the historical environmental impact of HNAs in East Asia and the characteristics of the Anthropocene.

Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements.

Based on the data of the State of Global Air (2020), air quality deterioration in Thailand has caused ~32,000 premature deaths, while the World Health Organization evaluated that air pollutants can decrease the life expectancy in the country by two years. PM2.5 was collected at three air quality observatory sites in Chiang-Mai, Bangkok, and Phuket, Thailand, from July 2020 to June 2021. The concentrations of 25 elements (Na, Mg, Al, Si, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Sr, Ba, and Pb) were quantitatively characterised using energy-dispersive X-ray fluorescence spectrometry. Potential adverse health impacts of some element exposures from inhaling PM2.5 were estimated by employing the hazard quotient and excess lifetime cancer risk. Higher cancer risks were detected in PM2.5 samples collected at the sampling site in Bangkok, indicating that vehicle exhaust adversely impacts human health. Principal component analysis suggests that traffic emissions, crustal inputs coupled with maritime aerosols, and construction dust were the three main potential sources of PM2.5. Artificial neural networks underlined agricultural waste burning and relative humidity as two major factors controlling the air quality of Thailand.

Near-gapless and haplotype-resolved apple genomes provide insights into the genetic basis of rootstock-induced dwarfing.

Dwarfing rootstocks have transformed the production of cultivated apples; however, the genetic basis of rootstock-induced dwarfing remains largely unclear. We have assembled chromosome-level, near-gapless and haplotype-resolved genomes for the popular dwarfing rootstock 'M9', the semi-vigorous rootstock 'MM106' and 'Fuji', one of the most commonly grown apple cultivars. The apple orthologue of auxin response factor 3 (MdARF3) is in the Dw1 region of 'M9', the major locus for rootstock-induced dwarfing. Comparing 'M9' and 'MM106' genomes revealed a 9,723-bp allele-specific long terminal repeat retrotransposon/gypsy insertion, DwTE, located upstream of MdARF3. DwTE is cosegregated with the dwarfing trait in two segregating populations, suggesting its prospective utility in future dwarfing rootstock breeding. In addition, our pipeline discovered mobile mRNAs that may contribute to the development of dwarfed scion architecture. Our research provides valuable genomic resources and applicable methodology, which have the potential to accelerate breeding dwarfing rootstocks for apple and other perennial woody fruit trees.

The impacts of regional transport on anthropogenic source contributions of PM2.5 in a basin city, China.

PM2.5 pollution events are often happened in urban agglomeration locates in mountain-basin regions due to the complex terra and intensive emissions. Source apportionment is essential for identifying the pollution sources and important for developing local mitigation strategies, however, it is influenced by regional transport. To understand how the regional transport influences the atmospheric environment of a basin, we connected the PM2.5 source contributions estimated by observation-based receptor source apportionment and the regional contributions estimated by a tagging technology in the comprehensive air quality model with extensions (CAMx) via an artificial neural network (ANNs). The result shows that the PM2.5 in Xi'an was from biomass burning, coal combustion, traffic related emissions, mineral dust, industrial emissions, secondary nitrate and sulfate. 48.8 % of the PM2.5 in study period was from Xi'an, then followed by the outside area of Guanzhong basin (28.2 %), Xianyang (14.6 %) and Weinan (5.8 %). Baoji and Tongchuan contributed trivial amount. The sensitivity analysis showed that the transported PM2.5 would lead to divergent results of source contributions at Xi'an. The transported PM2.5 from the outside has great a potential to alter the source contributions implying a large uncertainty of the source apportionment introduced when long-range transported pollutants arrived. It suggests that a full comprehension on the impacts of regional transport can lower the uncertainty of the local PM2.5 source apportionment and reginal collaborative actions can be of great use for pollution mitigation.

Biogas production prediction model of food waste anaerobic digestion for energy optimization using mixup data augmentation-based global attention mechanism.

Achieving rapid, efficient, and cost-effective anaerobic digestion (AD) of food waste is a key means to improve the efficiency of food waste treatment. However, in view of the shortage of historical anaerobic digestion data, the limitation of general neural networks in predicting biogas production, and its sensitivity to abnormal variation points, achieving accurate prediction of biogas production is not easy. This paper proposes a novel biogas production prediction model of food waste AD for energy optimization based on the mixup data augmentation integrating an improved global attention mechanism long short-term memory (LSTM). Taking the AD data of the actual factory as samples, the mixup data augmentation is introduced to generate virtual samples with the similar distribution as original samples. Then original samples and generated virtual samples are used as the input of the global attention mechanism LSTM to establish the food waste AD biogas production prediction model. Finally, the proposed method is applied in the biogas production prediction of actual food waste treatment plants. Compared with other industrial modeling models, the experimental results show that the proposed method has the highest prediction accuracy of 0.988, which performs well in predicting biogas production and can effectively guide and timely adjust feed configuration of AD plants.

Characteristics and health risk assessment of indoor and outdoor PM2.5 in a rural village, in Northeast of China: impact of coal and biomass burning.

Fine particulate matter (PM2.5) has health effects that may depend on its sources and chemical composition. In this study, characteristics of PM2.5 chemical composition and health risk assessment from Songyuan, China, were investigated during day and night in indoor and outdoor from February 4 to 19, 2021. Relative high concentrations of PM2.5 were obtained in indoor environment than outdoor, with 503.95 ± 209.62 µg/m3 during the day and 357.52 ± 232.81 µg/m3 at night for the indoor environment. Relatively high total carbon, organic carbons, elemental carbons, polycyclic aromatic hydrocarbons (PAHs), and oxygenated polycyclic aromatic hydrocarbons (OPAHs) were obtained in indoor environment. However, the average concentrations of PAHs were higher during night (73.57 ± 43.09 ng/m3) in indoor and OPAHs during day (6.027 ± 2.960 ng/m3) in outdoor. They had different I/O distributions of these compounds during day and night. Indeno(1,2,3-cd) pyrene was the dominant PAHs, and benzanthrone was the dominant OPAHs; this is different with the previous studies. The high indoor/outdoor ratios showed the indoor coal and biomass burning greatly affect the indoor pollutants. Average ILCR health risk assessment for PAHs was all higher than 10-6 for different age gender, suggesting there has potential cancer risk existed for populations living in the rural coal and biomass burning area Songyuan, China.

Microstructural Understanding of Flow Accelerated Corrosion of SA106B Carbon Steel in High-Temperature Water with Different Flow Velocities.

All light or heavy water reactors fabricated with carbon steels suffer from flow-accelerated corrosion (FAC). The FAC degradation of SA106B with different flow velocities was investigated in terms of microstructure. As flow velocity increased, the major corrosion type changed from general corrosion to localized corrosion. Severe localized corrosion occurred in the pearlite zone, which can be the prior location for generating pits. After normalizing, the improvement in microstructure homogeneity reduced the oxidation kinetics and lowered cracking sensitivity, causing a decrease in FAC rates of 33.28%, 22.47%, 22.15%, and 17.53% at flow velocity of 0 m/s, 1.63 m/s, 2.99 m/s, and 4.34 m/s, respectively. Additionally, localized corrosion tendency was decreased by reducing the micro-galvanic effect and tensile stresses in oxide film. The maximum localized corrosion rate decreased by 21.7%, 13.5%, 13.8%, and 25.4% at flow velocity of 0 m/s, 1.63 m/s, 2.99 m/s, and 4.34 m/s, respectively.

Sediment records of black carbon variations over the last two centuries in North China.

Reconstructing historical black carbon (BC) variations based on sedimentary records are significant for understanding long-term BC emissions, tracing sources, and establishing effective strategies for pollution control. By comparing BC profiles between four lake sediment cores, historical BC variations were reconstructed on the southeastern Mongolian Plateau in North China. Except one, the other three records show close fluxes and similar temporal trends of soot, indicating their repetitiveness on revealing historical variations at a regional scale. Unlike soot, char and BC in these records, derived mainly from local sources, reflected the occurrence of natural fires and human activities near the lakes. Before the ∼1940s, these records showed no well-established anthropogenic BC signals except some occasional natural-related increases. This was different from the global BC increased since the Industrial Revolution, indicating a negligible influence of transboundary BC on the region. Anthropogenic BC in the region had increased since the 1940s-1950s ascribed to emissions from Inner Mongolia and nearby provinces. The increases were moderate in the 1950s-1970s, corresponded with the initial development of industry after the founding of the P.R. China. The most pronounced BC increases occurred in 1980s-2016, commensurate with rapid socio-economic development after the Reform and Opening-up in 1978. Different from model estimations on Chinese BC emissions, our records show unexpected BC increases in recent two decades caused by pollutant emission rises in this undeveloped region. This suggests that BC emissions in relatively small cities and rural areas in China were likely underestimated and their role on national BC cycling needs to be reassessed.

Heterogeneous characteristics and absorption enhancement of refractory black carbon in an urban city of China.

In this study, field measurement was conducted using an integrated online monitoring system to characterize heterogeneous properties and light absorption of refractory black carbon (rBC). rBC particles are mainly from the incomplete combustion of carbonaceous fuels. With the data collected from a single particle soot photometer, thickly coated (BCkc) and thinly coated (BCnc) particles are characterized with their lag times. With different responses to the precipitation, a dramatical decline of 83 % in the number concentration of BCkc is shown after rainfall, while that of BCnc decreases by 39 %. There is a contrast in core size distribution that BCkc is always with larger particle sizes but has smaller core mass median diameters (MMD) than BCnc. The mean rBC-containing particle mass absorption cross-section (MAC) is 6.70 ± 1.52 m2 g-1, while the corresponding rBC core is 4.90 ± 1.02 m2 g-1. Interestingly, there are wide variations in the core MAC values which range by 57 % from 3.79 to 5.95 m2 g-1, which are also closely related to those of the whole rBC-containing particles with a Pearson correlation of 0.58 (p < 0.01). Errors would be made if we eliminate the discrepancies and set the core MAC as a constant when calculating absorption enhancement (Eabs). In this study, the mean Eabs is 1.37 ± 0.11 while the source apportionment shows that there are five contributors of Eabs including secondary aging (37 %), coal combustion (26 %), fugitive dust (15 %), biomass burning (13 %) and traffic-related emissions (9 %). Secondary aging is found to be the highest contributor due to the liquid phase reactions in formations of secondary inorganic aerosol. Our study characterizes property diversities and provides insights into the sources impacting the light absorption of rBC and will be helpful for controlling it in the future.

Novel risk assessment model of food quality and safety considering physical-chemical and pollutant indexes based on coefficient of variance integrating entropy weight.

Food safety is important for sustainable social and economic development and people's health. The traditional single risk assessment model is one-sided to the weight distribution of food safety factors including physical-chemical and pollutant indexes, which cannot comprehensively assess food safety risks. Therefore, a novel food safety risk assessment model combining the coefficient of variation (CV) integrating the entropy weight (EWM) (CV-EWM) is proposed in this paper. The CV and the EWM are used to calculate the objective weight of each index with physical-chemical and pollutant indexes effecting food safety, respectively. Then, the weights determined by the EWM and the CV are coupled by the Lagrange multiplier method. The ratio of the square root of the product of two weights and the weighted sum of the square root of the product are regarded as the combined weight. Thus, the CV-EWM risk assessment model is constructed to comprehensively assess the food safety risk. Moreover, the Spearman rank correlation coefficient method is used to test the compatibility of the risk assessment model. Finally, the proposed risk assessment model is applied to evaluate the quality and safety risk of sterilized milk. By analyzing the attribute weight and comprehensive risk value of physical-chemical and pollutant indexes effecting the sterilized milk quality, the results show that this proposed model can scientifically obtain the weight of physical-chemical and pollutant indexes to objectively and reasonably evaluate the overall risk of food, which has certain practical value for discovering the influencing factors of risk occurrence to risk prevention and control of food quality and safety.

Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion.

The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3-0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.

Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network.

Nowadays, the world has achieved tremendous economic development at the expense of the long-term habitability of the planet. With the rapid economic development, the global greenhouse effect caused by excessive carbon dioxide (CO2) emissions is also accumulating, which generates the negative impact of global warming on nature and human beings. Meanwhile, economy and CO2 emissions prediction methods based on traditional neural networks lead to gradient disappearance or gradient explosion, making the economy and CO2 emissions prediction inaccurate. Therefore, this paper proposes a novel economy and CO2 emissions prediction model based on a residual neural network (RESNET) to optimize and analyze energy structures of different countries or regions in the world. The skip links are used in the inner residual block of the RESNET to alleviate vanishing gradients due to increasing depth in deep neural networks. Consequently, the proposed RESNET can optimize this problem and protect the integrity of information by directly bypassing the input information to the output, which can increase the precision of the prediction model. The needs for natural gas, hydroelectricity, oil, coal, nuclear energy, and renewable energy in 24 different countries or regions from 2009 to 2020 are used as inputs, the CO2 emissions and the gross domestic product (GDP) per capita are respectively used as the undesired output and the desired output of the RESNET to build an economy and CO2 emissions prediction model. The experimental results show that the RESNET has higher correctness and functionality than the traditional convolutional neural network (CNN), the radial basis function (RBF), the extreme learning machine (ELM) and the back propagation (BP). Furthermore, the proposed model provides guidance and development plans for countries or regions with low energy efficiency, which can improve energy efficiency, economic development and reasonably control CO2 emissions.

Concentrations and source identification of priority polycyclic aromatic hydrocarbons in sediment cores from south and northeast Thailand.

In this study, the environmental fate of carcinogenic polycyclic aromatic hydrocarbons (PAHs) in tropical lake sediments and their potential sources have been discussed. 15 PAHs (i.e. ΣPAH) have been investigated in two lakes, namely Songkhla Lake (SKL) and Nong Han Kumphawapi Lake (NHL), which are located at the southern and north-eastern parts of Thailand, respectively. Since these two lakes are registered as important wetlands under the Ramsar convention (United Nations Educational, Scientific and Cultural Organization: UNESCO), the quantitative identification of potential contributors of PAHs is an inevitable analytical tool for launching an evidence-based policy. The ΣPAH concentrations observed in SKL and NHL sediments (n = 135) were in the range of 19.4-1,218 ng g-1 and 94.5-1,112 ng g-1, respectively. While the exponential decline of ΣPAH contents were detected in SKL sediments, NHL showed a trend of enhancing PAH contents with depth. The averaged benzo [a]pyrene (B [a]P) contents of surface sediments in both lakes were much below the value stipulated by the United States Environmental Protection Agency (US-EPA) guidelines for carcinogen risk assessment. Based on numerous multivariate statistical techniques coupled with source apportionment analysis, "biomass burning" and "anthropogenic activities" are two potential contributors of the PAHs detected in the study areas. To achieve the long-term conservation of nature with related ecosystem services and cultural values, it is therefore important to promote decision-making based on ecotoxicological studies of carcinogenic substances.

Plant-scale biogas production prediction based on multiple hybrid machine learning technique.

The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23-45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750-3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.

Sedimentary records of human activities in China over the past two millennia and implications for the Anthropocene: A review.

Human activities have profoundly transformed the natural environment and the Earth system, leading to the concept of the Anthropocene. This paper summarizes the effects of human activities on the environment in China as recorded in sedimentary archives. China is divided into core and marginal areas based on their natural and societal conditions, and changes in selected proxies for four stages since 2.0 ka are assessed. From 2.0 to 1.0 ka, tree pollen ratios, magnetic susceptibility values, stable organic carbon isotope ratios, and lead concentrations began to deviate from natural baseline (4.0-2.0 ka) values in the core area at different times depending on location. From 1.0 ka to 1950 CE, anthropogenic perturbations recorded by these proxies increased and exhibited regional changes in the core area. From 1950 to1980 CE, total organic carbon contents, stable organic carbon isotope ratios, total nitrogen contents, and stable nitrogen isotope ratios changed significantly in both the core and marginal areas. After 1980 CE, lead concentrations, black carbon and polycyclic aromatic hydrocarbons contents increased rapidly. In the latter two stages, the amounts and chronologies of artificial radionuclides and novel materials in the strata reflect their history of outputs or emissions. The boundaries for each stage correspond with important historical events. At 1.0 ka, the political center of China moved eastward, and a transportation network was established in the core area. In ca. 1950 CE, the People's Republic of China was established and the Global Acceleration began, while 1980 CE corresponds with the Reform and Opening-up of China that led to an accelerated industrialization. Our review shows that transportation networks and industries were key factors for intensification of human activities that caused Earth system to enter the Anthropocene.

Assessment of the inhalation exposure and incremental lifetime cancer risk of PM2.5 bounded polycyclic aromatic hydrocarbons (PAHs) by different toxic equivalent factors and occupancy probability, in the case of Xi'an.

Polycyclic aromatic hydrocarbons (PAHs) are widespread toxic pollutants in the atmosphere and have attracted much attention for decades. In this study, we compared the health risks of PAHs based on different toxic equivalent factors (TEFs) in a heavily polluted area during heating and non-heating periods. We also pay attention to occupancy probability (OP) in different polluted areas. The results showed that there were big differences for calculations by different TEFs, and also by OP or not. Age groups except adults were all lower calculated by OP than not. The sensitivity analysis results on the incremental lifetime cancer risks (ILCR) for population groups by Monte Carlo simulation identified that the cancer slope factor extremely affected the health risk assessment in heating periods, followed by daily inhalation exposure levels. However, daily inhalation exposure levels have dominated the effect on the inhalation ILCR and then followed by the cancer slope factor in non-heating periods. The big differences by different calculations investigated that it is important to set up the correlations between the pollution level and health risks, especially for the longtime health assessment.

Impact of reduced anthropogenic emissions on chemical characteristics of urban aerosol by individual particle analysis.

A single particle aerosol mass spectrometer was deployed in a heavily polluted area of China during a coronavirus lockdown to explore the impact of reduced anthropogenic emissions on the chemical composition, size distributions, mixing state, and secondary formation of urban aerosols. Ten particle groups were identified using an adaptive resonance network algorithm. Increased atmospheric oxidation during the lockdown period (LP) resulted in a 42.2%-54% increase in the major NaK-SN particle fraction relative to the normal period (NP). In contrast, EC-aged particles decreased from 31.5% (NP) to 23.7% (LP), possibly due to lower emissions from motor vehicles and coal combustion. The peak particle size diameter increased from 440 nm during the NP to 500 nm during LP due to secondary particle formation. High proportions of mixed 62NO3- indicate extensive particle aging. Correlations between secondary organic (43C2H3O+, oxalate) and secondary inorganic species (62NO3-, 97HSO4- and 18NH4+) versus oxidants (Ox = O3 + NO2) and relative humidity (RH) indicate that increased atmospheric oxidation promoted the generation of secondary species, while the effects of RH were more complex. Differences between the NP and LP show that reductions in primary emissions had a remarkable impact on the aerosol particles. This study provides new insights into the effects of pollution emissions on atmospheric reactions and the specific aerosol types in urban regions.