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Effect estimates of prenatal exposure to ambient PM2.5 on change in grams (β) of birth weight among term births (≥37 weeks of gestation; term birth weight, TBW) vary widely across studies. We present the first systematic review and meta-analysis of evidence regarding these associations. Sixty-two studies met the eligibility criteria for this review, and 31 studies were included in the meta-analysis. Random-effects meta-analysis was used to assess the quantitative relationships. Subgroup analyses were performed to gain insight into heterogeneity derived from exposure assessment methods (grouped by land use regression [LUR]-models, aerosol optical depth [AOD]-based models, interpolation/dispersion/Bayesian models, and data from monitoring stations), study regions, and concentrations of PM2.5 exposure. The overall pooled estimate involving 23,925,941 newborns showed that TBW was negatively associated with PM2.5 exposure (per 10 μg/m3 increment) during the entire pregnancy (β = −16.54 g), but with high heterogeneity (I2 = 95.6%). The effect estimate in the LUR-models subgroup (β = −16.77 g) was the closest to the overall estimate and with less heterogeneity (I2 = 18.3%) than in the other subgroups of AOD-based models (β = −41.58 g; I2 = 95.6%), interpolation/dispersion models (β = −10.78 g; I2 = 86.6%), and data from monitoring stations (β = −11.53 g; I2 = 97.3%). Even PM2.5 exposure levels of lower than 10 μg/m3 (the WHO air quality guideline value) had adverse effects on TBW. The LUR-models subgroup was the only subgroup that obtained similar significant of negative associations during the three trimesters as the overall trimester-specific analyses. In conclusion, TBW was negatively associated with maternal PM2.5 exposures during the entire pregnancy and each trimester. More studies based on relatively standardized exposure assessment methods need to be conducted to further understand the precise susceptible exposure time windows and potential mechanisms.

To investigate the spatial and vertical distribution of atmospheric pollutants (SO2, NOx, CO and O3), aircraft-based measurements (model: Yun-12, 12 flights, 27 h total flight time) were conducted from near the surface up to 2400 m over the Beijing-Tianjin-Hebei (BTH) region between June 17th and July 22nd 2016. The results showed that high concentrations of primary gaseous pollutants (SO2, NOx, CO) were generally present in Beijing, Tianjin, Langfang and Tangshan areas, while high values of O3 frequently appeared in areas far from the city. The flights at noon and dusk measured higher O3 concentrations at 600 m and lower O3 concentrations at higher altitudes, implying a strong influence by photochemical production. Back trajectory analysis suggested that the high levels of gaseous pollutants, especially at 600 m, were associated with pollution sources transported from the southerly direction during the observation period. The first simultaneous vertical distribution measurements using aircraft and tethered balloon were conducted in Gaocun (a rural site between Beijing and Tianjin) on June 17th. The results indicated that an inversion layer at the top of the planetary boundary layer (PBL) significantly suppressed vertical exchange through the PBL and resulted in a “two-layer” vertical distribution of pollutants above and below the PBL. Additionally, a residual high O3 layer (79.9 ± 2.5 ppb, 500–1000 m) was observed above the PBL, and it contributed to the surface peak O3 level at noon through downward transport along with the opening up of the PBL. These results indicate that coupled effects of horizontal and vertical transport should be investigated in future studies to improve the chemical transport models used to study the vertical distribution and regional transport over the BTH region.

Using central site measurement data to predict personal exposure to particulate matter (PM) is challenging, because people spend most of their time indoors and ambient contribution to personal exposure is subject to infiltration conditions affected by many factors. Efforts in assessing and predicting exposure on the basis of associated indoor/outdoor and central site monitoring were limited in China. This study collected daily personal exposure, residential indoor/outdoor and community central site PM filter samples in an elderly community during the non-heating and heating periods in 2009 in Tianjin, China. Based on the chemical analysis results of particulate species, mass concentrations of the particulate compounds were estimated and used to reconstruct the PM mass for mass balance analysis. The infiltration factors (Finf) of particulate compounds were estimated using both robust regression and mixed effect regression methods, and further estimated the exposure factor (Fpex) according to participants' time-activity patterns. Then an empirical exposure model was developed to predict personal exposure to PM and particulate compounds as the sum of ambient and non-ambient contributions. Results showed that PM mass observed during the heating period could be well represented through chemical mass reconstruction, because unidentified mass was minimal. Excluding the high observations (>300μg/m3), this empirical exposure model performed well for PM and elemental carbon (EC) that had few indoor sources. These results support the use of Fpex as an indicator for ambient contribution predictions, and the use of empirical non-ambient contribution to assess exposure to particulate compounds.

Cohort evidence that links long-term exposures to air pollution and mortality comes largely from the United States and European countries. We investigated the relationship between long-term exposures to particulate matter

Because ambient ozone (O3) has fine spatial scale variability in addition to a large scale regional distribution, accurate exposure predictions for population health studies need to also capture fine spatial scale differences in exposure. To address these needs, we developed a 3-year average land use regression (LUR) and combined LUR and Bayesian maximum entropy (BME) by incorporating a national area variability LUR model for China from 2015 to 2017 along with data that take into account incompleteness of O3 monitoring data into a BME framework. Spatio-temporal kriging models that either included or did not include “soft” data were used for comparison. The final LUR model included five predictor variables: road length within a 1000 m buffer, temperature, wind speed, industrial land area within a 3000 m buffer and altitude. The 1-year predicted O3 concentrations based on the ratio method moderately agreed with the measured concentration, and the regression R2 values were 0.53, 0.57 and 0.59 in the year of 2015, 2016 and 2017, respectively. The LUR/BME model performed better (R2 = 0.80, root mean squared error [RMSE] = 23.5 μg/m3) than the ordinary spatio-temporal kriging model that either included “soft” data (R2 = 0.57, RMSE = 49.2 μg/m3) or did not include the “soft” data (R2 = 0.52, RMSE = 58.5 μg/m3). We have demonstrated that a hybrid LUR/BME model can provide accurate predictions of O3 concentrations with high spatio-temporal resolution at the national scale in mainland China.

Accurate exposure estimate of the air pollutant PM2.5 is required to evaluate its health impacts in epidemiological studies, due to its adverse effects on human's respiratory and cardiovascular systems. However, traditional personal sampling is time and cost consuming. Thus, modeling techniques are needed to accurately predict the personal exposure level to PM2.5. In this study, a total of 117 older adults over 60 were recruited in Tianjin, a heavily polluted city in northern China, for indoor, outdoor and personal PM2.5 sampling. Eighteen variables which may increase the exposure level of older adults were recorded for artificial neural network (ANN) simulation. Four modeling techniques, including time-integrated activity modeling, Monte Carlo simulation, ANN modeling, and combined use of principal component analysis (PCA) and ANN model, were used to evaluate their ability for predicting real exposure values of PM2.5. The results of traditional time-weighted activity modeling showed the lowest correlation with measured values with R2 of 0.57 and 0.42 in winter and summer, respectively. For Monte Carlo simulation, high correlation was obtained (R2 of 0.93 and 0.92 in winter and summer, respectively) between percentiles of the predicted and the real exposure values. Compared with the simple ANN models, the combined use of PCA and ANN produced the most accurate results with R2 of 0.99 and RMSE lower than 15. Since the information of the input variables for the PCA-ANN model can be obtained from the questionnaire and fixed air quality monitoring sites, this technique shows a great potential in predicting personal exposure level to the air pollutant because no additional concentration measurement is needed.

Tracking of reactive nitrogen (N) sources is important for the effective mitigation of N emissions. By combining the N and oxygen (O) isotopes of atmospheric NO3−, stable isotope mixing models were recently applied to evaluate the relative contributions of major NOx sources. However, it has long been unresolved how to accurately constrain the δ15N differences between NO3− and corresponding NOx (e(NO2→NO3−) values). Here, we first incorporated the HC oxidation (NO2 → NO3−) pathway by using Δ17O values to evaluate the e(NO2→NO3−) values, performed on NO3− in PM2.5 collected during the day and at night from January 4–13, 2015 at an urban site in Beijing. We found that the Δ17O-based e values (e17O-based(NO2→NO3−)) (15.6 ± 7.4‰) differed distinctly from δ18O-based e values (e18O-based(NO2→NO3−)) (33.0 ± 9.5‰) so did not properly incorporate the isotopic effects of the HC oxidation (NO2 → NO3−) pathway. Based on the e(NO2→NO3−) values, δ15N values of NOx from coal combustion (CC), vehicle exhausts (VE), biomass burning (BB), and the microbial N cycle (MC), as well as NO3− in PM2.5, we further quantified the source contributions by using Stable Isotope Analysis in R (the SIAR model). We found that the respective fractional contributions of CC-NOx and MC-NOx were underestimated by 64% and were overestimated by 216% by using e18O-based(NO2→NO3−) values. We concluded that the new e17O-based(NO2→NO3−) values reduced uncertainties in contribution analysis and the evaluation method for atmospheric NO3− sources.

107 kinds of C 2 –C 12 volatile organic compound (VOC) mass concentrations and profiles for four types of coal-fired stationary sources in Liaoning Province were studied by a dilution sampling system and GC–MS analysis method, which are of significant importance with regard to VOC emissions in northeast of China. The results showed that there were some differences among these VOC source profiles. The total mass concentrations of analyzed 107 VOC species varied from 10,917 to 19,652 μg m − 3 . Halogenated hydrocarbons exhibited higher mass percentages for the VOC source profiles of iron smelt (48.8%) and coke production plant (37.7%). Aromatic hydrocarbons were the most abundant in heating station plant (69.1%). Ketones, alcohols and acetates held 45.0% of total VOCs in thermal power plant. For non-methane hydrocarbons (NMHCs), which are demanded for photochemical assessment in the USA, toluene and n-hexane were the most abundant species in the iron smelt, coke production and thermal power plant, with the mass percentages of 64.8%, 52.7% and 38.6%, respectively. Trimethylbenzene, n-propylbenzene and o,m-ethyltoluene approximately accounted for 70.0% in heating station plant. NMHCs emitted from coke production, iron smelt, heating station and power plant listed above presented different chemical reactivities. The average OH loss rate of NMHCs from heating station, was 4 to 5.6 times higher than that of NMHCs from iron smelt, coke production and power plant, which implies that VOCs emitted from heating station in northeast of China should be controlled firstly to avoid photochemical ozone pollution and protect human health. There are significant variations in the ratios of benzene/toluene and m, p-xylene/ethylbenzene of these coal-fired source profiles. The representativeness of the coal-fired sources studied and the VOC samples collected should be more closely examined. The accuracy of VOC source profiles related to coal-fired processes is highly dependent on location and sampling method.

The wind erosion source is the greatest contributor of PM10 in Tianjin, China. It is difficult to establish a PM10 emission inventory for the wind erosion source because of the complicated conditions affecting PM10 emissions from wind erosion. In this study, a novel method is developed to establish a PM10 emission inventory for the wind erosion source by using the Wind Erosion Prediction System (WEPS) model with 1 × 1 km spatial resolution. In 2011, the average annual emission of PM10 in Tianjin was 0.373 tons km− 2 (1.113 tons km− 2 in the heating period, 0.237 tons km− 2 in the sand period, and 0.013 tons km− 2 in the non-heating period), and the total PM10 emission was 904,871.6 tons (221,080.7 tons in the heating period, 52,977.8 tons in the sand period, and 1953.2 tons in the non-heating period). After establishing the PM10 emission inventory for the wind erosion source in this manner, the data were input into Model-3/SMOKE. The contribution of the PM10 concentration from the wind erosion source was estimated using the Community Multi-scale Air Quality (Model-3/CMAQ) model and was compared with the monitoring value and the source apportionment results using the chemical mass balance (CMB) method. For this comparison, over the entire year, the R2 was 0.711. More specifically, R2 was 0.664, 0.733, and 0.679 in the heating, non-heating, and sand periods, respectively. Thus, we have obtained a reliable method for estimating the source of PM10 from wind erosion.

The emission factors and compositions of volatile organic compounds (VOC) in exhaust gas from in-use gasoline passenger cars were characterized using a chassis dynamometer. Three passenger cars were tested at the ECE and the EUDC drive cycles to represent both urban and suburban driving scenarios. Exhaust gas was collected in Summa canisters and analyzed by gas chromatography-mass spectrometry (GC-MS). Common gaseous emissions (CH(4), NOx, CO, and CO(2)) were measured by an on-board monitoring system. The VOC emission factors of different cars ranged from 0.10 to 0.25 g km(-1) at the ECE cycle, and 0.01-0.02 g km(-1) at the EUDC cycle. A total of 57 individual VOC were detected in the exhaust gas, and the weight percentages were very consistent among the three cars. Ethylene (11.80 wt.%), toluene (11.27 wt.%), and benzene (8.83 wt.%) were the most abundant VOC in exhaust gas. Aromatics (38.32%) dominated the low speed conditions (ECE), while alkanes (37.34%) were the major compounds at the high speed condition (EUDC). The total amount of alkenes did not change much between those two cycles, while ethylene is abundant in the ECE and EUDC cycles. Ozone formation potential (OFP) was calculated to estimate the ozone yield from VOC emissions by gasoline cars and the results showed that OFP of VOC emission at the ECE cycle was about ten times higher than that at the EUDC cycle.

Volatile Organic Compounds (VOCs) exposure can induce a range of adverse human health effects. To date, however, personal VOCs exposure and residential indoor and outdoor VOCs levels have not been well characterized in the mainland of China, less is known about health risk of personal exposure to VOCs. In this study, personal exposures for 12 participants as well as residential indoor/outdoor, workplace and in vehicle VOCs concentrations were measured simultaneously in Tianjin, China. All VOCs samples were collected using passive samplers for 5 days and were analyzed using Thermal Desorption GC-MS method. U.S. Environmental Protect Agency's Inhalation Unit Risks were used to calculate the inhalation cancer health risk. To assess uncertainty of health risk estimate, Monte Carlo simulation and sensitivity analysis were implemented. Personal exposures were greater than residential indoor exposures as expected with the exception of carbon tetrachloride. Exposure assessment showed modeled and measured concentrations are statistically linearly correlated for all VOCs (P