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In this study, we developed an approach that integrated multiple patterns of timescale for box modeling (MCMv3.3.1) to better understand the O3–precursor relationship at multiple sites and through continuous observations. A 5-month field campaign was conducted in the summer of 2019 to investigate the ozone formation chemistry at three sites in a major prefecture-level city (Zibo) in Shandong Province of northern China. It was found that the relative incremental reactivity (RIR) of major precursor groups (e.g., anthropogenic volatile organic compounds (AVOCs), NOx) was overall consistent in terms of timescales changed from wider to narrower (four patterns: 5-month, monthly, weekly, and daily) at each site, though the magnitudes of RIR varied at different sites. The time series of the photochemical regime (using RIRNOx / RIRAVOC as an indicator) in weekly or daily patterns further showed a synchronous temporal trend among the three sites, while the magnitude of RIRNOx / RIRAVOC was site-to-site dependent. The derived RIR ranking (top 10) of individual AVOC species showed consistency between three patterns (i.e., 5-month, monthly, and weekly). It was further found that the campaign-averaging photochemical regimes showed overall consistency in the sign but non-negligible variability among the four patterns of timescale, which was mainly due to the embedded uncertainty in the model input dataset when averaging individual daily patterns into different timescales. This implies that utilizing narrower timescales (i.e., daily pattern) is useful for deriving reliable and robust O3–precursor relationships. Our results highlight the importance of quantifying the impact of different timescales to constrain the photochemical regime, which can formulate more accurate policy-relevant guidance for O3 pollution control.

In this study, we developed an approach that integrating multiple patterns of time scale for box modeling (MCMv3.3.1) to better understand the O3-precursor relationship through multiple-site and continuous observations. A five-month field campaign was conducted in the summer of 2019 to investigate the ozone formation chemistry at three sites in a major prefecture-level city (Zibo) in Shandong province of northern China. It was found that the relative incremental reactivity (RIR) of major precursor groups (e.g., anthropogenic volatile organic compound (AVOC), NOx) were overall consistent along with time scale (four patterns: five-month, monthly, weekly, and daily) varied from wider to narrower, though the magnitude of RIR varied at each site. The time series of the photochemical regime (using RIRNOx/RIRAVOC as indicator) in weekly or daily patterns further showed varied magnitude but a synchronous temporal trend among the three sites. The derived RIR ranking (top 10) of individual AVOC species showed consistency at three averaged patterns (i.e., five-month, monthly, and weekly). It was further found that the campaign-averaging photochemical regimes showed overall consistency but non-negligible variability among the four patterns of time scale, which was mainly due to the embedded uncertainty in model input dataset when averaging individual daily pattern into different timescales. This implies that integrating multiple patterns of time scale is useful to derive reliable and robust O3-precursor relationship. Our results highlight the importance of quantifying the impact of different time scales to constrain the photochemical regime, which can formulate more accurate policy-relevant guidance for O3 pollution control.

To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory (LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated, and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error (RMSE), scatter index (SI) and mean absolute error (MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.

Polycyclic aromatic hydrocarbons (PAHs) have carcinogenic impacts on human health. However, limited studies are available on the characteristics, sources, and source-specific health risks of PM2.5-bound PAHs based on personal exposure data, and comparisons of the contributions of indoor and outdoor sources are also lacking. We recruited 101 senior citizens in the winter of 2011 for personal PM2.5 sample collection. Fourteen PAHs were analyzed, potential sources were apportioned using positive matrix factorization (PMF), and inhalational carcinogenic risks of each source were estimated. Six emission sources were identified, including coal combustion, gasoline emission, diesel emission, biomass burning, cooking, and environmental tobacco smoking (ETS). The contribution to carcinogenic risk of each source occurred in the following sequence: biomass burning > diesel emission > gasoline emission > ETS > coal combustion > cooking. Moreover, the contributions of biomass burning, diesel emission, ETS, and indoor sources (sum of cooking and ETS) to PAH-induced carcinogenic risk were higher than those to the PAH mass concentration, suggesting severe carcinogenic risk per unit contribution. This study revealed the contribution of indoor and outdoor sources to mass concentration and carcinogenic risk of PM2.5-bound PAHs, which could act as a guide to mitigate the exposure level and risk of PM2.5-bound PAHs.

From April 2008 to July 2009, ambient measurements of 58 volatile organic compounds (VOCs), including alkanes, alkenes, and aromatics, were conducted in nine industrial cities (Shenyang, Fushun, Changchun, Jilin, Harbin, Daqing, Huludao, Anshan and Tianjin) of the Northeast Region, China (NRC). Daqing had the highest concentration of VOCs (519.68 ± 309.88 μg/m3), followed by Changchun (345.01 ± 170.52 μg/m3), Harbin (231.14 ± 46.69 μg/m3), Jilin (221.63 ± 34.32 μg/m3), Huludao (195.92 ± 103.26 μg/m3), Fushun (135.43 ± 46.01 μg/m3), Anshan (109.68 ± 23.27 μg/m3), Tianjin (104.31 ± 46.04 μg/m3), Shenyang (75.2 ± 40.09 μg/m3). Alkanes constituted the largest percentage (>40%) in concentrations of the quantified VOCs in NRC, and the exception was Tianjin dominated by aromatics (about 52.34%). Although alkanes were the most abundant VOCs at the cities, the most important VOCs contributing to ozone formation potential (OFP) were alkenes and aromatics. Changchun had the highest OFP (537.3 μg/m3), Tianjin had the lowest OFP (111.7 μg/m3). The main active species contributing to OFP in the nine cities were C2~C6 alkanes, C7~C8 aromatic hydrocarbons, individual cities (Daqing) contained n-hexane, propane and other alkane species. Correlation between individual hydrocarbons, B/T ratio and principal component analysis model (PCA) were deployed to explore the source contributions. The results showed that the source of vehicle exhausts was one of the primary sources of VOCs in all nine cities. Additionally, individual cities, such as Daqing, petrochemical industry was founded to be an important source of VOCs. The results gained from this study provided a large of useful information for better understanding the characteristics and sources of ambient VOCs incities of NRC. The non-carcinogenic risk values of the nine cities were within the safe range recognized by the U.S. Environmental Protection Agency (HQ < 1), and the lifetime carcinogenic risk values of benzene were 3.82 × 10−5~1.28 × 10−4, which were higher than the safety range specified by the US Environmental Protection Agency (R < 1.00 × 10−6). The results of risk values indicated that there was a risk of cancer in these cities.

Systemic inflammation is a key mechanism in the development of cardiovascular diseases induced by exposure to fine particles (particles with aerodynamic diameter ≤2.5 μm [PM

Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-meter interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R(2) and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.

In order to understand the compositions characteristics of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) fraction in road dust (RD2.5) of oasis cities on the edge of Tarim Basin, 30 road dust (RD) samples were collected in Kashi, Cele, and Yutian in the spring, 2018, and RD2.5 was collected using the resuspension approach. Eight water-soluble ions, 39 trace elements and 8 fractions of carbon-containing species in PM2.5 were analyzed. Ca2+ and Ca were the most abundant ions and elements in RD2.5 (7.1% and 9.5%). Cl- in RD2.5 was affected not only by attributed to saline-alkali soils in oasis cities of the Tarim Basin and dust from Taklimakan Desert but also by human activities. Moreover, the organic carbon/elemental carbon (OC/EC) ratio indicated that carbon components in RD2.5 in Cele town mainly come from fossil fuel combustion, while those in Yutian and Kashi mainly come from biomass combustion. It is noteworthy that high Ca in RD2.5 was seriously affected by anthropogenic emissions, and high Na and K contents in RD2.5 could be derived from soil and desert dust. It was estimated that Cd, Tl, Sn and Cr were emitted from anthropogenic emissions using the enrichment factor. The coefficients of divergence (COD) result indicated that the influence of local emission on road dust emission is greater than that of long-distance transmission. This study is the first time to comprehensively analyze the chemical characteristics of road dust in oasis cities, and the results provides the sources of road dust at the margin of Tarim Basin.

Amines are considered as an emerging class of atmospheric pollutants that are of great importance to atmospheric chemistry and new particle formation. As a typical amine, 2-amino-2-methyl-1-propanol (AMP) is one of the proposed solvents for capturing CO2 from flue gas streams in amine-based post-combustion CO2 capture plants, and it is expected to result in AMP emission and secondary product formation in the atmosphere. However, the current knowledge of its atmospheric chemistry and kinetics is poorly understood, particularly in a reactive environment. In this work, we used the CSIRO smog chamber to study the photo-oxidation of AMP in the presence of volatile organic compound (VOC)-NOx surrogate mixtures over a range of initial amine concentrations. O3 formation was significantly inhibited when AMP was added to the surrogate VOC-NOx mixtures, implying that AMP could alter known atmospheric chemical reaction pathways and the prevailing reactivity. Simultaneously, a large amount of AMP-derived secondary aerosol was formed, with a considerably high aerosol mass yield (i.e., ratio of aerosol formed to reacted AMP) of 1.06 ± 0.20. Based on updated knowledge of its kinetics, oxidation pathways, and product yields, we have developed a new mechanism (designated as CSIAMP-19), integrated it into the Carbon Bond 6 (CB6) chemical mechanism, and evaluated it against available smog chamber data. Compared with the existing AMP mechanism (designated as CarterAMP-08), the modified CB6 with CSIAMP-19 mechanism improves prediction against AMP-VOC-NOx experiments across a range of initial AMP concentrations, within ±10% model error for gross ozone production. Our results contribute to scientific understanding of AMP photochemistry and to the development of the chemical mechanism of other amines. Once some potential limitations are considered, the updated AMP reaction scheme can be further embedded into the chemical transport model for regional modeling scenarios where AMP-related emissions are of concern.

The association between oxidative protein damage in early pregnant women and ambient fine particulate matter (PM

To demonstrate the chemical characteristics and carcinogenic risk of environmental tobacco smoke (ETS) emission, replicate PM2.5 samples were collected via volunteers smoking nine different brands of Chinese cigarettes. The source profile of ETS was established with an abundance of inorganic and organic chemical compositions, source signatures were recognized, and a comparison of excess cancer risk of heavy metals versus polycyclic aromatic hydrocarbons (PAHs) was completed. The results showed that organic carbon (OC), element carbon (EC) and potassium (K) were the primary components in the ETS profile; chromium (Cr), lead (Pb) and cadmium (Cd) were the most abundant heavy metals; and PAHs were mainly distributed in middle molecular weight (MMW) and high molecular weight (HMW) ranges. Benz(a)anthracene (BaA), benzo(b)fluoranthene (BbF) and chrysene (CHR) could distinguish ETS from cooking/outdoor stationary sources as source-related markers. The ranking of excess cancer risk of heavy metals was Cr > Cd > arsenic (As) > Pb > nickel (Ni) and that of PAHs was benzo(a)pyrene (BaP) > BaA > BbF > indeno(1,2,3-cd)pyrene (IND) > CHR. The comparison revealed that the totality of excess cancer risk of heavy metals was 785 times higher than that of PAHs, indicating that the harmful health effect caused by inorganic compositions during ETS exposure should attract public attention.

The association between ambient fine particulate matter (PM

Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM

The effect of fine particulate matter (PM

Short-term exposure to traffic-related air pollution (TRAP) are associated with reduced lung function. However, TRAP is a mixture of various gaseous pollutants and particulate matter (PM), and therefore it is unknown that which components of TRAP are responsible for the respiratory toxicity. Using a powered air-purifying respirator (PAPR), we conducted a randomized, double-blind, crossover trial in which 40 adults were exposed to TRAP for 2 h at the sidewalk of a busy road. During the exposure, the participants wore the PAPR fitted with a PM filter, a PM and volatile organic compounds (VOCs) filter, or a sham filter (no filtration, Sham mode). The participants were blinded to the type of filter in their PAPR, and experienced three exposures, once for each intervention mode in random order. We measured two lung function measures (forced expiratory volume in 1 s [FEV1] and forced vital capacity [FVC]) and an airway inflammation marker (fraction of exhaled nitric oxide [FE

Effect estimates of prenatal exposure to ambient PM

Heavy air pollution attacked Beijing and its surrounding areas in January of 2013, which attracted large attentions from around the world. In this study, we conducted highly time-resolved measurements of inorganic ions associated with PM2.5 at an urban site in Beijing during this period. We applied the curve fitting method, the quantile regression model and the probability function model to evaluate the relationship between secondary inorganic ions (sulfate, nitrate and ammonium, SNA) and meteorological parameters (relative humidity (RH) and temperature). In our model results, RH was regarded as one of the key factors of high concentrations of SNA, and high level of RH would enhance the concentrations of SNA. In addition, the effect of temperature was also important and noticeable. We further constructed a probability function model to investigate the joint effects of RH and temperature. The model results showed higher RH and a temperature of approximately −5 °C (−4∼-6 °C for sulfate and nitrate; −5∼-7 °C for ammonium) would be the most suitable conditions for the high concentrations of SNA during the air pollution episodes in Beijing.

To evaluate the potential benefits of biomass use for air pollution control, this paper identified and quantified the emissions of major reactive organic compounds anticipated from biomass-fired industrial boilers. Wood pellets (WP) and straw pellets (SP) were burned to determine the volatile organic compound emission profiles for each biomass-boiler combination. More than 100 types of volatile organic compounds (VOCs) were measured from the two biomass boilers. The measured VOC species included alkanes, alkenes and acetylenes, aromatics, halocarbons and carbonyls. A single coal-fired boiler (CB) was also studied to provide a basis for comparison. Biomass boiler 1 (BB1) emitted relatively high proportions of alkanes (28.9%–38.1% by mass) and alkenes and acetylenes (23.4%–40.8%), while biomass boiler 2 (BB2) emitted relatively high proportions of aromatics (27.9%–29.2%) and oxygenated VOCs (33.0%–44.8%). The total VOC (TVOC) emission factors from BB1 (128.59–146.16 mg/kg) were higher than those from BB2 (41.26–85.29 mg/kg). The total ozone formation potential (OFP) ranged from 6.26 to 81.75 mg/m3 with an average of 33.66 mg/m3 for the two biomass boilers. The total secondary organic aerosol potential (SOAP) ranged from 61.56 to 211.67 mg/m3 with an average of 142.27 mg/m3 for the two biomass boilers. The emission factors (EFs) of TVOCs from biomass boilers in this study were similar to those for industrial coal-fired boilers with the same thermal power. These data can supplement existing VOC emission factors for biomass combustion and thus enrich the VOC emission inventory.

Modeling fine-scale spatial and temporal patterns of air pollutants can be challenging. Advanced spatio-temporal modeling methods were used to predict both long-term and short-term concentrations of six criteria air pollutants (particulate matter with aerodynamic diameter less than or equal to 10 and 2.5 μm [PM10 and PM2.5], SO2, NO2, ozone and carbon monoxide [CO]) in Beijing, China. Monitoring data for the six criteria pollutants from April 2014 through December 2017 were obtained from 23 administrative monitoring sites in Beijing. The dimensions of a large array of geographic covariates were reduced using partial least squares (PLS) regression. A land use regression (LUR) model in a universal kriging framework was used to estimate pollutant concentrations over space and time. Prediction ability of the models was determined using leave-one-out cross-validation (LOOCV). Prediction accuracy of the spatio-temporal two-week averages was excellent for all of the pollutants, with LOOCV mean squared error-based R2 (R2mse) of 0.86, 0.95, 0.90, 0.82, 0.94 and 0.95 for PM10, PM2.5, SO2, NO2, ozone and CO, respectively. These models find ready application in making fine-scale exposure predictions for members of cohort health studies and may reduce exposure measurement error relative to other modeling approaches.

Nitrate (NO3−) is a key component of secondary inorganic aerosols and PM2.5. However, the contributions of nitrogen oxides (NOx) emission sources to NO3− in PM2.5 remain poorly constrained. This study measured nitrogen (N) isotopes of NO3− (hereafter as δ15N-NO3-) in PM2.5 collected at Beijing in 2014. We observed that δ15N-NO3- values in PM2.5 (−2.3‰ − 19.7‰; 7.3 ± 5.4‰ annually) were significantly higher in winter (11.9 ± 4.4‰) than in summer (2.2 ± 2.5‰). The δ15N differences between source NOx and NO3− in PM2.5 (hereafter as Δ values) were estimated by a computation module as 7.8 ± 2.2‰ − 10.4 ± 1.6‰ (8.8 ± 2.4‰). Using the Δ values and δ15N values of NOx from major fossil (coal combustion, vehicle exhausts) and non-fossil sources (biomass burning, microbial N cycle), contributions of major NOx sources to NO3− in PM2.5 were further estimated by the SIAR model. We found that seasonal variations of δ15N-NO3- values in PM2.5 of Beijing were mainly caused by those of NOx contributions from coal combustion (38 ± 10% in winter, 20 ± 9% in summer). Annually, NOx from coal combustion, vehicle exhausts, biomass burning, and microbial N cycle contributed 28 ± 12%, 29 ± 17%, 27 ± 15%, and 16 ± 7% to NO3− in PM2.5, respectively, showing actually comparable contributions between non-fossil NOx (43 ± 16%) and fossil NOx (57 ± 21%). These results are useful for planning the reduction of NOx emissions in city environments and for elucidating relationships between regional NOx emissions and atmospheric NO3− pollution or deposition.

The high concentrations of particulate matter (PM10 and PM2.5) air pollutants in China are of great concern to public health. To develop efficient and effective environment control policies, an understanding of PM background concentrations is therefore essential. In this study, we estimated background concentrations of PM10 and PM2.5 in Beijing from January to December 2016, using an integrated approach that involved meteorological filtering, backward trajectory modelling, spectral analysis and a Kolmogorov–Zurbenko (KZ) filter. The estimated annual average background concentration of PM10 was 47 μg/m3 (ranging from 37 to 71 μg/m3), while PM2.5 averaged 25 μg/m3 (ranging from 18 to 37 μg/m3). The contribution of background pollution to the ambient concentration of PM was 37–68% for PM10 and 21–54% for PM2.5, which is comparable to other studies. The estimated background concentrations from this study were assessed using the Community Multi-scale Air Quality results published by Beijing Municipal Environmental Protection Bureau. Our method has great potential for estimating PM pollutants and could also be applied to other pollutants.

Background: Experimental research suggests that fine particulate matter (PM2.5) exposure might affect embryonic development. However, only few population-based studies have investigated the impact of maternal exposure to PM2.5 on the early pregnancy loss. Objectives: To estimate associations between clinically recognized early pregnancy loss (CREPL) and exposure to ambient PM2.5 at individual residences during peri-conception periods, with the aim to identify susceptible exposure time windows. Methods: CREPL cases and normal early pregnancy controls (of similar age and gravidity presenting within one week, a total of 364 pairs) were recruited between July 2017 and July 2018 among women residing in Tianjin, China. Average ambient PM2.5 concentrations of ten exposure windows (4 weeks, 2 weeks and 1 week before conception; the first, second, third and fourth single week, the first and second 2-week periods, and the entire 4-week period after conception) at the women's residential addresses were estimated using temporally-adjusted land use regression models. Associations between PM2.5 exposures at specific peri-conception time windows and CREPL were examined using conditional logistic regression models, adjusted for covariates. Results: Based on adjusted models, CREPL was significantly associated with a 10 μg/m3 increase in PM2.5 exposure during the second week after conception (OR = 1.15; 95% CI: 1.04, 1.27; p = 0.005), independent of effects at other time windows. There was also an association of CREPL with PM2.5 during the entire 4-week period after conception (OR = 1.22; 95% CI: 1.02, 1.46; p = 0.027). There was little evidence for associations with exposure during pre-conception exposure windows. Conclusions: Maternal exposures to ambient PM2.5 during a critical time window following conception are associated with CREPL, with the second week after conception possibly being the exposure window of most vulnerability. Future studies should focus on replicating these findings and on pathogenic mechanisms. Keywords: Adverse pregnancy outcomes, Air pollution, Land use regression models, Miscarriage, Particulate matter

Quantitative evaluation of background concentrations of pollutants in atmospheric field can help us implement effective measures to improve air quality. The aim of this study was to estimate background concentrations of PM10 and PM2.5 in Xinjiang using meteorological filter and the Eckhardt algorithm. In this study, the meteorological factors including wind direction, wind speed and temperature were used to filter the background periods. Results showed that annual average background concentrations of PM10 and PM2.5 in Xinjiang were 112 μg/m3 and 39 μg/m3 in 2016, respectively. There were seasonal differences on background concentrations of PMs in Northern and Southern Xinjiang, and the background concentrations in Southern Xinjiang were much higher than those in Northern Xinjiang. Background concentrations from easterly wind (49 μg/m3 for PM10 and 29 μg/m3 for PM2.5) and westerly wind (179 μg/m3 for PM10 and 63 μg/m3 for PM2.5) were the highest in Northern Xinjiang and Southern Xinjiang, respectively. Dust from Taklimakan Desert had great contribution to the background concentrations of PMs in Southern Xinjiang area. Good correlations were obtained when comparing the two methods. The differences of annual average background concentrations in Xinjiang between two methods were 9 μg/m3 for PM10 and 4 μg/m3 for PM2.5. The estimated background concentrations by the proposed method can be used by local government to develop more efficient pollution control policies.