Response of a simulated aquatic fungal community to nanoplastics exposure and functional consequence on leaf decomposition.

Nanoplastics pose a potential threat to a wide variety of aquatic organisms. Despite the awareness of this existing hazard, the impact of nanoplastics on natural fungal communities remains a research gap. In this study, five dominant fungi species, isolated from a stream ecosystem, were used to explore the effects of different nano-polystyrene (nano-PS) particles concentrations on a simulated fungal community. Specifically, the evaluation was conducted regarding the fungal growth, reproductivity, structural composition, and ecological function in leaf litter decomposition. A 15-day exposure experiment showed that 100 µg/L nano-PS significantly reduced the microcosm pH. The extracellular enzyme activities of ß-glucosidase, leucine-aminopeptidase, and peroxidase were significantly promoted by nano-PS exposure for 5 days or 15 days. Total sporulation rate significantly decreased after the 15-day exposure to 1 and 100 µg/L nano-PS and significantly increased under 10 µg/L nano-PS. In contrast, nano-PS concentrations had no effects on fungal biomass. In addition, the reduced relative abundance of Geotrichum candidum lowered its contribution to leaf decomposition, resulting in a decreased litter decomposition rate of a 24.5-27.9 % after exposure. This suggests that 1-100 µg/L nano-PS inhibited leaf decomposition by inhibiting fungal reproduction and reducing the contribution of specific fungal species. In addition, the findings highlight the importance of exploring the potential mechanisms of the interaction between nanoplastics and fungal species.

Polystyrene size-dependent impacts on microbial decomposers and nutrient cycling in streams.

The particle size of plastic is one of the most important factors influencing its ecotoxicity, but we are unclear about the effect of polystyrene (PS) particle size on microbial decomposers and consequent nutrient cycling in streams. Here, using microcosm experiments, we assessed how three PS sizes (50 nm, 1 µm, and 20 µm) influenced the process and consequences of leaf litter decomposition. Under acute exposure to 1 µm and 20 µm PS, fungal biomass significantly decreased, but microbial biomass significantly increased, indicating compensations may work between fungi and other microbial decomposers. After chronic exposure to 50 nm and 1 µm PS, the leaf decomposition rate decreased by 19.27 % and 15.22 %, respectively, due to the reduced microbial enzyme activity, fungal diversity, and dominance of Anguillospora. As a result, the regeneration of nutrients, especially phosphorus, was significantly depressed, which might influence the primary productivity of streams. Therefore, our results suggest that nanoscale PS has a greater impact on microbial activity, thus affecting their functioning in leaf litter decomposition and consequent nutrient cycling. The findings provide a data support for the risk assessment of plastic pollution in freshwater systems.

Hourly emissions of air pollutants and greenhouse gases from open biomass burning in China during 2016-2020.

Open biomass burning (OBB) is a significant source of air pollutants and greenhouse gases that have contributed to air pollution episodes in China in recent years. An accurate emission inventory is critical for the precise control of OBB. Existing OBB emission datasets are commonly based on MODIS observations, and most only have a daily-scale temporal resolution. Daily OBB emissions, however, might not accurately represent diurnal variations, peak hours, or any open burning processes. The China Hourly Open Biomass Burning Emissions (CHOBE) dataset for mainland China from 2016 to 2020 was developed in this study using the spatiotemporal fusion of multiple active fires from MODIS, VIIRS S-NPP and Himawari-8 AHI detections. At a spatial resolution of 2 km, CHOBE provided gridded CO, NOx, SO2, NH3, VOCs, PM2.5, CO2, CH4 and N2O emissions from OBB. CHOBE will enhance insight into OBB spatiotemporal variability, improves air quality and climate modelling and forecasting, and aids in the formulation of precise OBB preventive and control measures.

Developing a model-ready highly resolved HONO emission inventory in Guangdong using domestic measured emission factors.

Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (OH) in the atmosphere. However, current chemical transport models (CTMs) typically underestimate ambient concentrations of HONO due to a dearth of high resolution primary HONO emission inventories. To address this issue, we have established a highly resolved bottom-up HONO emission inventory for CTMs in Guangdong province, utilizing the best available domestic measured emission factors and newly obtained activity data. Our results indicate that emissions from various sources in 2020, including soil, on-road traffic, non-road traffic, biomass burning, and stationary combustion, were estimated at 21.5, 10.0, 8.2, 2.5, and 0.7 kt, respectively. Notably, the HONO emissions structure differed between the Pearl River Delta (PRD) and the non-PRD regions. Specifically, traffic sources were the dominant contributors (62 %) to HONO emissions in the PRD, whereas soil sources accounted for the majority (65 %) of those in the non-PRD. Among on-road traffic sources, diesel vehicles played a significant role, contributing 99.7 %. Comparisons with previous methods suggest that HONO emissions from diesel vehicles are underestimated by approximately 2.5 times. Higher HONO emissions, dominated by soil emissions, were observed in summer months, particularly in August. Furthermore, diesel vehicle emissions were pronounced at night, likely contributing to the nighttime accumulation of HONO and the morning peak of OH. The emission inventories developed in this study can be directly applied to widely used CTMs, such as CMAQ, CAMx, WRF-Chem, and NAQPMS, to support the simulation of OH formation and secondary air pollution.

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China.

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.

Near-real-time estimation of hourly open biomass burning emissions in China using multiple satellite retrievals.

Open biomass burning (OBB) is an important source of air pollutants and greenhouse gases, but its dynamic emission estimation remains challenging. Existing OBB emission datasets normally provide daily estimates based upon Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals but tend to underestimate the emissions due to the coarse spatial resolution and sparse observation frequency. In this study, we proposed a novel approach to improve OBB emission estimations by fusing multiple active fires detected by MODIS, Visible Infrared Imaging Radiometer onboard the Suomi National Polar-orbiting Partnership (VIIRS S-NPP) and Himawari-8. The fusion of multiple active fires can capture the missing small fires and the large fires take place during the non-overpass time of MODIS observations. Also, regional-based fire radiative power (FRP) cycle reconstruction models and OBB emission coefficients were developed to address the large spatial discrepancies of OBB emission estimations across China and to promote the estimate to an hourly resolution. Using the new approach, hourly gridded OBB emissions in China were developed and can be updated with a lag of 1-day, or even near-real-time when real-time multiple active fires are available. OBB emissions in China based on this approach were more than 3 times of those in previous datasets. Evaluations revealed that the spatial distribution of the estimated PM2.5 emissions from this study was more consistent with the ambient PM2.5 concentrations during several episodes than existing datasets. The hourly OBB emissions provide new insight into its spatiotemporal variations, enhance timely and reliable air quality modeling and forecast, and support the formulation of accurate prevention and control policies of OBB.

Dual-carbon isotope constraints on source apportionment of black carbon in the megacity Guangzhou of the Pearl River Delta region, China for 2018 autumn season.

Black carbon (BC) aerosol negatively affects air quality and contributes to climate warming globally. However, little is known about the relative contributions of different source control measures to BC reduction owing to the lack of powerful source-diagnostic tools. We combine the fingerprints of dual-carbon isotope using an optimized Bayesian Markov chain Monte Carlo (MCMC) scheme and for the first time to study the key sources of BC in megacity Guangzhou of the Pearl River Delta (PRD) region, China in 2018 autumn season. The MCMC model-derived source apportionment of BC shows that the dominant contributor is petroleum combustion (39%), followed by coal combustion (34%) and biomass burning (27%). It should be noted that the BC source pattern is highly sensitive to the variations of air masses transported with an enhanced contribution of fossil source from the eastern area, suggesting the important impact of regional atmospheric transportation on the BC source profile in the PRD region. Also, we further found that fossil fuel combustion BC contributed 84% to the total BC reduction during 2013-2018. The response of PM2.5 concentration to the 14C-derived BC source apportionment is successfully fitted (r = 0.90) and the results predicted that it would take ∼6 years to reach the WHO PM2.5 guideline value (10 µg m-3) for the PRD region if the emission control measures keep same as they are at present. Taken together, our findings suggest that dual-carbon isotope is a powerful tool in constraining the source apportionment of BC for the evaluations of air pollution control and carbon emission measures.

A newly integrated dataset of volatile organic compounds (VOCs) source profiles and implications for the future development of VOCs profiles in China.

Volatile organic compounds (VOCs) source profiles can be used for a number of purposes, such as creating speciated air pollutant emission inventories and providing inputs to receptor and air quality models. In this study, we first collected and schematically evaluated more than 500 Chinese domestic source profiles from literature and field measurements, and then established a most up-to-date dataset of VOCs source profiles in China by integrating 363 selective VOCs profiles into 101 sector-based source profiles. The profile dataset covers eight major source categories and contains 447 VOCs species including non-methane hydrocarbons (NMHCs) species and oxygenated VOCs (OVOCs) species. The results shown that (1) VOCs composition characteristics exhibit variations for most Level-II source sectors and Level-III sub-sectors even under the same Level-I source category; (2) OVOCs, which were significantly missing in previous profiles, account for more than 95% in cooking and 20- 40% in non-road mobile, biomass burning and solvent use sources; (3) aromatics account for 20%-40% in most emission sources except cooking source, alkenes and alkynes account for ~20% in combustion sources (stationary combustion, mobile source and biomass burning), alkanes are abundant in gasoline-related emission sources(on-road mobile source and fuel oil storage and transportation); (4) missing OVOCs species could bring 30%-50% to ozone formation potentials in most emission sources; and (5) there are considerable differences in VOCs chemical groups and individual species for most emission sources between this dataset and the widely used U.S. SPECIATE database, indicating the importance of developing domestic VOCs source profiles. The dataset developed in this study can help support reactive VOCs species-based ozone control strategy and provide domestic profile data for source apportionment and air quality modeling in China and other countries or regions with similar emission source characteristics.

An updated model-ready emission inventory for Guangdong Province by incorporating big data and mapping onto multiple chemical mechanisms.

An accurate characterization of spatial-temporal emission patterns and speciation of volatile organic compounds (VOCs) for multiple chemical mechanisms is important to improving the air quality ensemble modeling. In this study, we developed a 2017-based high-resolution (3 km × 3 km) model-ready emission inventory for Guangdong Province (GD) by updating estimation methods, emission factors, activity data, and allocation profiles. In particular, a full-localized speciation profile dataset mapped to five chemical mechanisms was developed to promote the determination of VOC speciation, and two dynamic approaches based on big data were used to improve the estimation of ship emissions and open fire biomass burning (OFBB). Compared with previous emissions, more VOC emissions were classified as oxygenated volatile organic compound (OVOC) species, and their contributions to the total ozone formation potential (OFP) in the Pearl River Delta (PRD) region increased by 17%. Formaldehyde became the largest OFP species in GD, accounting for 11.6% of the total OFP, indicating that the model-ready emission inventory developed in this study is more reactive. The high spatial-temporal variability of ship sources and OFBB, which were previously underestimated, was also captured by using big data. Ship emissions during typhoon days and holidays decreased by 23-55%. 95% of OFBB emissions were concentrated in 9% of the GD area and 31% of the days in 2017, demonstrating their strong spatial-temporal variability. In addition, this study revealed that GD emissions have changed rapidly in recent years due to the leap-forward control measures implemented, and thus, they needed to be updated regularly. All of these updates led to a 5-17% decrease in the emission uncertainty for most pollutants. The results of this study provide a reference for how to reduce uncertainties in developing model-ready emission inventories.

Identification of long-term evolution of ozone sensitivity to precursors based on two-dimensional mutual verification.

Tropospheric ozone pollution has been continuously worsening in China during the past decade. Identification of long-term evolution of ozone sensitivity to precursors is essential to evaluating the impact of emission reduction measures on ozone pollution. Traditional observation-based model and 3-d numerical model are not suitable for analyzing long-term variation of ozone sensitivity to precursors. In this study, by transforming the conventional ozone isopleth plot into a VOCR isopleth plot in the functional space of NOx and ozone concentrations, we developed a novel approach to identify ozone sensitivity to precursors by simply using long-term monitoring data of ozone, NOx and temperature. This approach estimated ozone formation regimes (OFR) by ozone sensitivity to NOx and temperature separately, and the convergence of OFR serves as a way of mutual verification. We found that ozone formation was generally in the VOCR-limited or transitional regime in Shanghai, the largest metropolitan area in China. However, OFR was shifted to NOx-limited at Pudong station during 2017-19 due much to the stringent NOx emission control. OFR was also shifted to NOx-limited along with the increasing temperature. When temperature was over 30 °C, Shanghai was mostly in a NOx-limited OFR. This highlights that the NOx emission control measures need to be strengthened to reduce peak ozone levels more efficiently. Jinshan station exhibited a different trend with OFR shifted to VOCR-limited in 2017-19, which proved the effectiveness of VOCs emission control on petrochemical sector. However, OFR was shifted to NOx-limited when temperature was over 30 °C, suggesting more stringent VOCs emissions control should be targeted on days with higher temperature.

Regional discrepancies in spatiotemporal variations and driving forces of open crop residue burning emissions in China.

As a major source of secondary organic aerosol, open crop residue burning (OCB) emits a large number of trace gases and particulates to the atmosphere. Extensive OCB is detected during harvest seasons in most regions of China. Emissions from OCB have been widely investigated in China, but few studies have focused on regional discrepancies in spatiotemporal variations with a long timescale. In this study, emissions from OCB in three typical regions of China, including Northeast China, Chengdu-Chongqing and Guangdong, from 2003 to 2016, were estimated using a combination of statistical data and the Moderate Resolution Imaging Spectroradiometer (MODIS) observations, and regional discrepancies in spatiotemporal variations and driving forces were analyzed in detail. The results showed that OCB emissions of CO and PM2.5 in 2016 were 3.63 × 106 and 6.96 × 105 tons in Northeast China, 9.77 × 105 and 1.36 × 105 tons in Chengdu-Chongqing, and 1.24 × 105 and 1.19 × 104 tons in Guangdong, respectively. The OCB emissions in Northeast China maintained an overall increasing trend, which were mainly dominated by rural economic development and agricultural modernization, with great potential for reduction. In Chengdu-Chongqing, emissions increased until 2013 and then decreased, due to the impact of policies prohibiting OCB. In Guangdong, emissions showed annual fluctuations with a slightly decreasing trend influenced by metrological conditions and related policies. The analysis of the driving forces behind the regional discrepancies among these typical regions indicated that OCB emissions in China were characterized by uneven rural economic development, control policies, and natural conditions, suggesting that region-dependent control measures are needed for reducing OCB emissions. This study provided a better understanding of OCB emission characteristics in the three typical regions and revealed the diversity of OCB emission characteristics in China.

Refined 2013-based vehicle emission inventory and its spatial and temporal characteristics in Zhengzhou, China.

Vehicle emission is becoming one of the most important pollution sources because of the increase in vehicle population and activity in China. A more reasonable and complete vehicle emission inventory in Zhengzhou for the year 2013 was developed in this study. This inventory is suitable for local emission factors and vehicle kilometers of travel. Estimates show that the total carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NOX), particulate matter (PM2.5 and PM10) and sulfur dioxide (SO2) emissions were 291Gg, 35Gg, 106Gg, 6Gg, 7Gg, and 3Gg, respectively. Approximately 55% of CO and HC emissions were from light duty gasoline vehicles and normal gasoline motorcycles, whereas approximately 60% of NOX, PM2.5, PM10 and SO2 were from heavy duty diesel vehicles, heavy duty diesel trucks, and medium duty diesel trucks. The spatial distribution of emissions was allocated in grid cells based on a road network and traffic flows with a resolution of 1km×1km at different road types and locations, which shows that the six aforementioned air pollutants have similar characteristics in administrative districts. Emissions are mainly concentrated on the central grid cells of each part and in good agreement with line sources. The spatial characteristics were compared at a resolution of 3km×3km and in a population-based approach. The network approach yields better level estimates in this study. Meanwhile, the preliminary temporal profiles were also established for on-road mobile source.