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Dimethyl sulfide (DMS), a marine-derived trace gas, can influence atmospheric compositions and has an impact on the global climate. To date, obtaining continuous and coupled shipboard underway measurements of DMS in seawater and air has been challenging. To address this issue, we report a custom-made sampling device based on the purge-and-trap technique. This sampler, in combination with a time-of-flight mass spectrometer (TOF-MS), was successfully utilized to perform coupled shipboard underway measurements of DMS in surface seawater and air around western Antarctica during the 34th Chinese Antarctic Research Expedition from February 2018 to April 2018. The seawater and air streams were continuously introduced into the sampler unit and subsampled every 10 min. The limits of detection (LODs) of DMS in seawater and air were found to be 0.07 nM and 32 pptv, respectively. The variability in the DMS levels in the surface seawater and air can be distinguished and evaluated based on the variations in the DMS peaks. These results demonstrated that the sampling device was effective for consistent, sensitive underway measurements of DMS.

We apply the regional chemistry-transport model WRF-Chem to investigate the air pollution characteristics in three Chinese metropolitan areas (Beijing, Shanghai and Guangzhou) and assess the model performance in wintertime (January 2010) and summertime (July 2010) conditions. Three simulations utilizing the HTAPv2, REASv2 and MACCity anthropogenic emission inventories are conducted and compared with satellite and ground-based measurements to assess the sensitivity of the model predictions to anthropogenic emissions. Model-predicted surface meteorological fields are overall in good agreement with surface observations. However, the model tends to overestimate maximum surface wind speed, a feature also found in previous WRF-Chem modelling studies. Large discrepancies between the three emission inventories are found, specifically in the spatial distribution and the magnitude of the emissions (e.g. over 98%, 86% and 85% differences in some regions for carbon monoxide (CO), nitrogen oxide (NO) and organic carbon (OC) emissions, respectively). As a result, we find large differences between the simulations. When compared to satellite and surface observations, the HTAPv2-and REASv2-based simulations better reproduce the magnitude and spatial patterns of CO total columns and NO2 tropospheric columns derived from MOPITTv6 and GOME2 satellite observations, respectively. The simulations satisfactorily reproduce the air pollution characteristics in January 2010 as observed at surface monitoring stations in Beijing and Shanghai. The HTAPv2-based simulation better captures the pollution episode that occurred in Beijing in mid-January 2010, which is found to be related to a combination of different meteorological factors that led to the build-up of pollution. The high photochemical activity and convective turbulence in July 2010, in addition to differences in the emissions used, led to more pronounced differences between the simulations and lower statistical skills in particular for trace gases. This study shows that a considerable part of uncertainties in air quality model predictions for China can be attributed to uncertainties in current anthropogenic emission inventories. It highlights the need for more accurate and highly resolved emissions in order to improve the accuracy of model predictions. However, this study reveals that in some cases, model-observation discrepancies cannot be attributed only to inaccuracies in the emissions. For example, we found that regardless of the adopted emissions, all of the simulations fail to capture the temporal variability of particulate matter in Guangzhou during January 2010, indicating that factors other than emissions have a considerable influence on the atmospheric composition in this region. This work shows that inaccuracies in the predicted meteorological parameters (e.g. wind speed/direction, relative humidity, etc.) significantly affect the model performance, as found for example in the model predictions for Shanghai for July 2010, which show high and abrupt increases of NOx and particulates due to rapid decreases in wind speed in the model.

Crop residue burning has been proved to have negative impacts on regional atmospheric environment. In this study, an evidence-based statistical modeling framework was established to quantify potential impacts of crop residue burning in the North China Plain (NCP) on summertime tropospheric ozone increase over East Asia during 2004–2016. To assess the intensity of crop residue burning, fire occurrence counts derived from the MODerate-resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites were used as a proxy. Additionally, another six factors were employed as potent explanatory variables. Maximum covariance analysis was first applied to decouple spatiotemporal interactions between tropospheric ozone and each explanatory variable. Based on the decoupled modes, multivariate linear regression (MLR) and artificial neural network (ANN) were used to establish statistical relationships between tropospheric ozone and contributing factors, respectively. The results indicate that the ANN-based modeling scheme enables to approximate the observed tropospheric ozone variations better than MLR. Further investigations reveal that the summertime crop residue burning in the NCP is the predominant factor contributing to the observed additive tropospheric ozone increases over East Asia, yielding extra 8% tropospheric ozone elevation on average in June. Moreover, UV radiation and wind also played critical roles in modulating the observed tropospheric ozone variations therein. In general, the critical role of crop residue burning over the NCP in modulating summertime tropospheric ozone increase over East Asia have been well demonstrated based on the proposed evidenced-based modeling framework.

The long-term characteristics of submicron particles (PM1) over Shanghai and their contributing factors (including precursor gases and meteorological variables), as well as their impact on visibility, were investigated using in situ measurements from Jan 1st, 2015, to Dec 31st, 2016. A discretization method was introduced to identify the impact of each contributing factor on PM1. The results show that the annual mean PM1 concentration over Shanghai is ∼28 μgm−3, which accounts for 69% of fine particles (PM2.5). The PM1 concentration shows obvious temporal variations on the scales of days, weeks, months, and years. Its diurnal pattern shows higher values in the daytime (with two peaks) than in the nighttime, which is different from the pattern for PM2.5 with high/low values in the nighttime/daytime. During a week, the PM1 concentration is the lowest on Tuesday and the highest on Friday. The discretized approach reveals that PM1 shows good linear relationships with its gaseous precursors and with meteorological variables under most conditions. The concentration of PM1 increases with increases in SO2, NO2, and NO ( 30) ppb. PM1 are negatively correlated with precipitation intensity, relative humidity (RH, >35%), and wind speed (>1.5 ms-1), and their rates of decrease are 3.3, 0.26, and 5.9 μgm−3 per 1 mmh−1, 1%, and 1 ms-1, respectively. Other factors (e.g., temperature and pressure) show nonlinear relationships with PM1 concentration, presumably due to their indirect influence on the transport, formation, or accumulation of PM1. The PM1 concentration has a distinct impact on visibility, and the PM1/PM2.5 ratio is a key indicator to represent the impact of particulate matter hygroscopicity on visibility. The PM1/PM2.5 ratio shows an exponential relationship (i.e., PM1/PM2.5 = 0.76 [(1-RH)/(1–40%)]0.11) with RH with a determination coefficient of 0.98. This parameter combined with the PM2.5 concentration well describes the impact of particulate matter and its hygroscopicity on visibility.

Atmospheric ammonia plays an important role in haze formation in East China. In this study, long-term measurements of NH3 concentrations were implemented at urban, suburban, and tunnel sites in Shanghai, the largest city in East China. The average monthly ammonia concentrations at the urban site varied from 3.7 ppb to 14.5 ppb and exhibited the highest levels in summer and lowest levels in winter, indicating that the biological emissions and agriculture in the surrounding areas are important contributors. The suburban NH3 levels were significantly higher in autumn compared to those at the urban site, indicating the important contribution of agricultural activities. Regardless of the season, the difference of NH3 concentrations between the tunnel and urban sites remained almost constant. On average, the tunnel NH3 level was three times higher than that of the nearby urban site, indicating strong vehicle NH3 emissions in the tunnel. The tunnel NH3 levels on weekdays were comparable to those on weekends, a result that was in agreement with the daily average traffic volume. It was estimated that the vehicle emissions contributed 12.6–24.6% of the atmospheric NH3 in the urban area and 3.8–7.5% for the whole area of Shanghai. Our results suggest that vehicle NH3 emissions should be considered, although agricultural emissions are still more important for mitigating severe haze pollution during wintertime in the megacities of China.

The Regional Atmospheric Environmental Modeling System for eastern China (RAEMS) is an operational numerical system to forecast near surface atmospheric pollutants such as PM2.5 and O3 over the eastern China region. This system was based on the fully online coupled weather research and forecasting/chemistry (WRF-Chem) model. Anthropogenic emissions were based on the multi-resolution emission inventory for China (MEIC), and biogenic emissions were online calculated using model of emissions of gases and aerosols from nature (MEGAN2). Authorized by the China Meteorological Administration (CMA), this system started to provide operational forecast in 2013. With a large domain covering eastern China, the system produces daily 72-hr forecast. In this work, a comprehensive evaluation was carried out against measurements for two full years (2014–2015). Evaluation results show that the RAEMS is skillful in forecasting temporal variation and spatial distribution of major air pollutants over the eastern China region. The performance is consistent in different forecast length of 24 h, 48 h, and 72 h. About half of cities have correlation coefficients greater than 0.6 for PM2.5 and 0.7 for daily maximum 8-h averaged (DM8H) ozone. The forecasted PM2.5 is generally in good agreement with observed concentrations, with most cities having normalized mean biases (NMB) within ±25%. Forecasted ozone diurnal variation is very similar to that of observed, and makes small peak time error for DM8H ozone. It also shows good capability in capturing ozone pollution as indicated by high critical success indexes (CSI). The modeling system also exhibits acceptable performance for PM10, NO2, SO2, and CO. Meanwhile, degraded performance for PM2.5 is found under heavy polluted conditions, and there is a general over estimation in ozone concentrations.

To investigate the composition and possible sources of aerosol particles in Beijing urban area, a single particle aerosol mass spectrometer (SPAMS) was deployed from April 22 to May 4, 2011. 510,341 particles out of 2,953,200 sized particles were characterized by SPAMS in combination with the ART-2a neural network algorithm. The particles were classified as rich-K (39.79%), carbonaceous species (32.7%), industry metal (19.2%), dust (5.7%), and rich-Na (1.76%). Industrial emissions related particles, rich-Fe, rich-Pb, and K-nitrate, were the major components of aerosol particles during haze periods, which were mainly from the steel plants and metal smelting processes around Beijing. Under stagnant meterological conditions, these regional emissions have a vital effect on haze formation. Organic carbon (OC) particles were attributed to biomass burning. NaK-EC was likely to come from local traffic emissions. Internally mixed organic and elemental carbon (OCEC) was found to be from possible sources of local traffic emission and biomass burning. It was found that coarse dust particles were mainly composed of four different types of dust particles, dust-Si, dust-Ca, dust-Al, and dust-Ti. It is the first time that SPAMS was used to study a dust storm in Beijing. Our results showed that SPAMS could be a powerful tool in the identification and apportionment of aerosol sources in Beijing, providing useful reference information for environmental control and management.

An ensemble and enhanced PM10 (particulate matter with a diameter less than 10 μm) concentration forecast model was established in eastern China based on data from 2005 to 2009. The enhanced model consists of a single stepwise regression forecast model and a combined forecast model based on wavelet decomposition and stepwise regression. Six individual forecast results were obtained with a combined model that can predict PM10 concentrations at multiple scales. By decomposing variables into detailed and approximated components in six scales and with the application of stepwise regression, the best-fitted forecast models were established in each component of the different scales. Then, the predicted results of the detail and approximation components were reconstructed in each scale as the enhanced prediction. A regional model was established for eastern China. The accuracy rate of each forecasted result by the regional model was calculated using testing data from 2010 based on the needs of operational forecasting. Precision evaluations were also performed. A comparatively higher accuracy was obtained by the combined model. The advantage of predicting the PM10 concentration with the combined model had wide spatial and temporal suitability. An enhanced forecast model was established for each city of eastern China with improvements, where all the predicted results in each city were evaluated by the accuracy rate and precision validation. In each city, the best-fitted model with the highest precision was selected and combined in an ensemble. The ensemble and enhanced forecast model had a significant improvement in accuracy rate and the highest precision of PM10 concentration forecasting in eastern China.

The hygroscopic properties of submicrometer urban aerosol particles were studied during the 2009 Mirage-Shanghai Campaign. The urban aerosols were composed of more-hygroscopic and nearly-hydrophobic particles, together with a trace of less-hygroscopic particles. The mean hygroscopicity parameter κ of the more-hygroscopic mode varied in the range of 0.27–0.39 depending on particle size. The relative abundance of the more-hygroscopic particles at any size was ca. 70%, slightly increasing with particle size. The number fraction of the nearly-hydrophobic particles fluctuated between 0.1 and 0.4 daily, in accordance with traffic emissions and atmospheric diffusion. The results from relative humidity dependence on hygroscopic growth and chemical analysis of fine particles indicated that particulate nitrate formation through the homogenous gas-phase reaction was suppressed under ammonia-deficient atmosphere in summer whereas the equilibrium was broken by more available NH3 during adverse meteorological conditions.

The characteristics of Aerosol Optical Depth (AOD) and Angstrom exponent were analyzed and compared using Cimel sunphotometer data from 2007 to 2008 at five sites located in the Yangtze River Delta region of China. The simultaneous measurements between Lin’an and ZFU showed a very high consistency of AOD at all wavelengths. The differences are less than 0.02 for Angstrom exponent and AOD at all wavelengths. The mean values of AOD at 440 nm at the Pudong, Taihu and Lin’an were about 0.74 ± 0.43, 0.85 ± 0.46, and 0.89 ± 0.46, respectively. The mean values of Angstrom exponents were about 1.27 ± 0.30, 1.20 ± 0.28 and 1.32 ± 0.35, respectively. The variation of monthly averaged AOD over Pudong showed a single peak distribution, with the maximum value occurring in July (AOD 440nm 1.26 ± 0.61) and minimum in January (AOD 440nm 0.50 ± 0.27). However, the variations of monthly averaged AOD at Taihu and Lin’an showed a bi-modal distribution. There were peak values of AOD occurring in July (AOD 440nm 1.41 ± 0.49) and September (AOD 440nm 1.22 ± 0.52) for Taihu. For Lin’an, the two peak values of AOD occurred in June (AOD 440nm 1.17 ± 0.69) and September (AOD 440nm 1.28 ± 0.46). The AOD accumulated mainly between 0.30–0.90(68%), 0.30–1.20(75%) and 0.30–1.20 (∼75%) at Pudong, Taihu, and Lin’an, respectively. The Angstrom exponent accumulated mainly between 1.10–1.60 (75%), 1.10–1.50 (63%) and 1.20–1.60, 50% (50%) at Pudong, Taihu, and Lin’an, respectively. The synchronized observation showed that the AOD at Pudong was larger than those at Dongtan by 0.03, 0.03, 0.04, 0.07, and 0.08 at wavelengths of 1020 nm, 870 nm, 670 nm, 500 nm and 440 nm, respectively. The synchronized observations at Pudong, Taihu and Lin’an showed that the three stations had high level AOD with means at 440 nm about 0.68, 0.73, and 0.78, respectively. The relationship between MODIS retrieved and ground-based measured AOD shows good agreement with R 2 ranging from 0.68 to 0.79 at Pudong, Taihu, Lin’an and Dongtan. The MODIS results were overestimated comparing the ground measurements at Pudong, Taihu, and Dongtan but exceptional at Lin’an. The analysis results between aerosol optical properties and wind measurement at Pudong showed that the wind speed from the east correlates with the lower observed AOD. The back trajectory analysis indicates that more than 50% airmasses were from the marine area at Pudong, while back trajectories distribution is relatively homogeneous at Lin’an.

Since 2005, Shanghai Meteorological Bureau (SMB) has established an observational network for measuring VOC, NOx, O3 and aerosols in the Shanghai region. In this study, a rapid O3 changes from Aug/02/2007 to Aug/11/2007 was observed in the region. During this 10 day period, the noontime O3 maximum decreased from 100 to 130 ppbv to about 20–30 ppbv. In order to analyze the processes in controlling this rapid change of O3 during this short period, a newly developed regional chemical/dynamical model (WRF-Chem) is applied to study O3 variability in the Shanghai region. The model performances are evaluated by comparing the model calculation to the measurement. The result shows that the calculated magnitudes and diurnal variations of O3 are close to the measured results in city sites, but are underestimated at a rural petroleum industrial site, suggesting that the emissions from petroleum factories around this rural site are significantly underestimated and need to be improved. The calculated rapid changes of O3 concentrations, O3 precursors, and aerosols are consistent with the measured results, suggesting that the model is suitable to study the causes of this rapid O3 change. The model analysis indicates that weather conditions play important roles in controlling the surface O3 in the Shanghai region. During summer, there is a persistent sub-tropical high pressure system (SUBH) in southeast of Shanghai over Pacific Ocean. During the earlier time of the period (Aug/02–Aug/05), the SUBH system was weak, resulting in weak surface winds. With the calm winds, a noticeable noontime sea-breeze produced an inflow from ocean to land, generating a cycling pattern of wind directions. As a result, the high O3 concentrations were trapped in the Shanghai region, with a maximum concentration of 100–130 ppbv. By contrast, during the later time of the period (Aug/06–Aug/11), the SUBH was enhanced, resulting in strong surface winds. The high O3 concentrations formed in the city were rapidly transported to the downwind region of the city, resulting in low O3 concentrations in the Shanghai region. This study illustrates that the WRF-Chem model is a useful tool for studying the high variability of O3 concentrations in Shanghai, which has important implication for the prediction of high O3 concentration events in the city.

Shanghai Meteorological Bureau (SMB) has established a VOC laboratory and an observational station network for VOC, NOx and O3 measurements in the Shanghai region. The measurements became operational since 2005. In this study, we investigate spatial and temporal variability of O3 and its precursors (NOx and VOCs), and impacts of the precursors on O3 formation. A chemical mechanism model (NCAR-MM) is applied to assess the sensitivity of the O3 formation to NOx and VOC concentrations. The results show that O3 concentrations are higher in rural area than in center of the city, suggesting that there are O3 depression processes in center of city and air pollutant emissions are not favorable for the O3 chemical formation. The analysis suggests that the NO and NO2 concentrations in center of the city are lower during weekend than weekday due to less traffic and industrial activities. By contrast, the O3 concentrations are higher during weekend than weekday. During weekend, the reduction of NO2 leads to enhancement in OH radicals, resulting in the higher O3 production. This enhancement of weekend O3 is off-set by decrease in VOC concentrations. The chemical model study suggests that the decrease of NOx alone could result in a larger weekend O3 enhancement than the measured result. The model sensitivity study also suggests that O3 concentration decreases when NOx concentration is enhanced. Both model and measurement results indicate that the O3 formation is clearly under VOC-sensitive regime in the city of Shanghai. As a result, in the urban area, the O3 production is strongly depressed by high NOx concentrations, resulting in lower O3 concentration in center of the city than in rural area of the city. This study further shows that the highly reactive VOCs (aromatics) play important roles in determining the O3 formation. The ratio of toluene/benzene suggests that automobiles play important roles in the O3 formation in the city of Shanghai. Further increase in automobiles in Shanghai could lead to potential high O3 concentration in the future.