Global PM2.5 Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios.

Ambient fine particulate matter (PM2.5) has severe adverse health impacts, making it crucial to reduce PM2.5 exposure for public health. Meteorological and emissions factors, which considerably affect the PM2.5 concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM2.5 concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data. Based on the estimated PM2.5 concentrations, the future premature mortality burden was assessed using the Global Exposure Mortality Model. Our results reveal that SSP3-7.0 scenario is associated with the highest PM2.5 exposure, with a global concentration of 34.5 µg/m3 in 2100, while SSP1-2.6 scenario has the lowest exposure, with an estimated of 15.7 µg/m3 in 2100. PM2.5-related deaths for individuals under 75 years will decrease by 16.3 and 10.5% under SSP1-2.6 and SSP5-8.5, respectively, from 2030s to 2090s. However, premature mortality for elderly individuals (>75 years) will increase, causing the contrary trends of improved air quality and increased total PM2.5-related deaths in the four SSPs. Our results emphasize the need for stronger air pollution mitigation measures to offset the future burden posed by population age.

An improved decomposition method to differentiate meteorological and anthropogenic effects on air pollution: A national study in China during the COVID-19 lockdown period.

Although the effects of meteorological factors on severe air pollution have been extensively investigated, quantitative decomposition of the contributions of meteorology and anthropogenic factors remains a big challenge. The novel coronavirus disease 2019 (COVID-19) pandemic affords a unique opportunity to test decomposition method. Based on a wind decomposition method, this study outlined an improved method to differentiate complex meteorological and anthropogenic effects. The improved method was then applied to investigate the cause of unanticipated haze pollution in China during the COVID-19 lockdown period. Results from the wind decomposition method show that weakened winds increased PM2.5 concentrations in the Beijing-Tianjin area and northeastern China (e.g., by 3.19 µg/m3 in Beijing). Using the improved decomposition method, we found that the combined meteorological effect (e.g., drastically elevated humidity levels and weakened airflow) substantially increased PM2.5 concentrations in northern China: the most substantial increases were in the Beijing-Tianjin-Hebei region (e.g., by 26.79 µg/m3 in Beijing). On excluding the meteorological effects, PM2.5 concentrations substantially decreased across China (e.g., by 21.84 µg/m3 in Beijing), evidencing that the strict restrictions on human activities indeed decreased PM2.5 concentrations. The unfavorable meteorological conditions, however, overwhelmed the beneficial effects of emission reduction, causing the severe haze pollution. These results indicate that the integrated meteorological effects should be considered to differentiate the meteorological and anthropogenic effects on severe air pollution.

Estimations of Long-Term nss-SO42- and NO3- Wet Depositions over East Asia by Use of Ensemble Machine-Learning Method.

Wet deposition of non-sea-salt sulfate (nss-SO42-) and nitrate (NO3-), derived from anthropogenic emissions of SO2 and NOx, exerts adverse effects on ecosystems. In this work, an ensemble back-propagation neural network was proposed to estimate the long-term wet depositions of nss-SO42- (2005-2017) and NO3- (2001-2014) over East Asia in 10 km resolution. The R2 values for the 10-fold cross-validation of annual wet depositions of nss-SO42- and NO3- were 0.90 and 0.85, respectively. The hotspots of the wet deposition of these two acidic species span southwestern, central, and eastern China. The molar ratio of NO3- to nss-SO42- increased in 10 out of 12 analyzed East Asian countries from 2005 to 2014, which indicates that the acidity in rainwater shifts from the sulfur type to nitrogen type over most of the regions. The wet deposition on the four ecosystems (forest, grassland, cropland, and freshwater body) was also analyzed. Results showed that the nss-SO42- wet deposition on 25.5% of freshwater bodies in 2015 and NO3- wet deposition on 21.7% of grassland in 2014 exceeded the ecosystem empirical critical loads (25 kg/ha sulfate and 2 kg N/ha) in East Asia. Thus, more stringent and regionally collaborative sulfur and nitrogen emission-control measures are urgently needed to protect the ecosystem of East Asia.

A novel framework for decomposing PM2.5 variation and demographic change effects on human exposure using satellite observations.

Human exposure to PM2.5, represented by population-weighted mean PM2.5 concentration (cρ), declines under three conditions: (1) mean PM2.5 concentration declines, (2) PM2.5 concentration within urban areas goes through more of a decrease than within rural areas, or (3) city planning relocates people into cleaner areas. Decomposing these effects on human exposure is essential to guide future environmental policies. The lack of ground PM2.5 observations limits the assessment of human exposure to PM2.5 over China. This study proposed a novel diagnostic framework using satellite observations to decompose the variation in cρ resulting from change in the mean PM2.5 concentration, spatial difference in PM2.5 change, and demographic change. In this framework, we decomposed cρ into mean PM2.5 concentration (c0) and pollution-population-coincidence induced PM2.5 exposure (PPCE). We then used this framework to decompose the variation in cρ over China within three recent Five-Year Plans (FYPs) (2001-2015). The results showed that the decline in c0 reduced cρ in most provinces within the eleventh and twelfth FYPs. The spatial difference in PM2.5 change reduced the PPCE and cρ in most provinces within the tenth and twelfth FYPs, with the most substantial reduction rate of -3.64 µg m-3·yr-1 in Tianjin within the twelfth FYP. Rural-to-urban migration resulting from rapid urbanization, however, increased the PPCE and cρ (by as much as 0.22 µg m-3·yr-1) in all provinces except Taiwan within all three FYPs. The demographic change reduced cρ in Taiwan because of the migration of population into less polluted areas. To better reduce human exposure, it is recommended that control efforts further target populous residential areas and urbanization planning relocates people into less polluted areas. Our decomposition framework paves a new way to decompose the human exposure to other air pollutants in China and other regions.

Development of an integrated machine-learning and data assimilation framework for NOx emission inversion.

As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.

Estimation and variation analysis of secondary inorganic aerosols across the Greater Bay Area in 2005 and 2015.

As the concentrations of primary components of fine particulate matter (PM2.5) have substantially decreased, the contribution of secondary inorganic aerosols to PM2.5 pollution has become more prominent. Therefore, understanding the variations in and characteristics of secondary inorganic aerosols is vital to further reducing PM2.5 concentrations in the future. In this study, an ensemble back-propagation neural network model was built by combining 3D numerical models, observation data, and machine learning methods, to estimate the concentrations of secondary inorganic aerosols (SO2-4, NO-3, and NH+4) across the Greater Bay Area (GBA) in 2005 and 2015. The ensemble model provided a better estimation than the 3D numerical air quality model, with higher correlation coefficients (approximately 0.85) and lower root mean square errors. The model revealed that the concentrations of the SO2-4, NO-3, and NH+4 decreased by 1.91, 0.20, and 0.49 µg/m3, respectively, from 2005 to 2015. To investigate the oxidation and acidy of sulfate, the sulfur oxidation ratio (SOR), degree of sulfate neutralization (DSN), and particle neutralization ratio (PNR) were calculated and analyzed for 2005 and 2015 across the GBA region. The SOR slightly increased in summer, but decreased in other seasons in 2015, indicating the overall weaker sulfate chemical formation due to sulfur emission control measures. The increasing DSN and PNR indicated that more sulfate was neutralized due to reduced sulfur emission and increased ammonia availability. Our study suggests that more effort is needed to control ammonia emission to further reduce the concentrations of SO2-4, NO-3, and NH+4 across the GBA region in the future.

Impacts of urbanization and long-term meteorological variations on global PM2.5 and its associated health burden.

PM2.5 pollution has adverse health effects on humans. Urbanization and long-term meteorological variations play important roles in influencing the PM2.5 concentration and its associated health effects. Our results indicate that the urbanization process can enhance the PM2.5 concentration globally. The PM2.5-caused mortality density (deaths/100 km2) is also positively correlated with the urbanization degree in both developed and developing countries. The results from machine learning technique revealed that the meteorology-driven variation in PM2.5-caused health burden has increased with the increase in the urbanization degree from 1980 to 2018, suggesting that residents living in urban areas are more vulnerable to experiencing unfavorable meteorological conditions (e.g. low wind speed and planetary boundary layer height). The maximum difference in PM2.5-caused mortality due to the variation in annual meteorological conditions (between 2013 and 1986) was 270 600 (196 800-317 900). Our findings indicate an urgent need to understand the driving force behind the appearance of unfavorable meteorological situations and propose suitable climate mitigation measures.

Effect of bromine and iodine chemistry on tropospheric ozone over Asia-Pacific using the CMAQ model.

Recent studies have focused on the chemistry of tropospheric halogen species which are able to deplete tropospheric ozone (O3). In this study, the effect of bromine and iodine chemistry on tropospheric O3 within the annual cycle in Asia-Pacific is investigated using the CMAQ model with the newly embedded bromine and iodine chemistry and a blended and customized emission inventory considering marine halogen emission. Results indicate that the vertical profiles of bromine and iodine species show distinct features over land/ocean and daytime/nighttime, related to natural and anthropogenic emission distributions and photochemical reactions. The halogen-mediated O3 loss has a strong seasonal cycle, and reaches a maximum of -15.9 ppbv (-44.3%) over the ocean and -13.4 ppbv (-38.9%) over continental Asia among the four seasons. Changes in solar radiation, dominant wind direction, and nearshore chlorophyll-a accumulation all contribute to these seasonal differences. Based on the distances to the nearest coastline, the onshore and offshore features of tropospheric O3 loss caused by bromine and iodine chemistry are studied. Across a coastline-centric 400-km-wide belt from onshore to offshore, averaged maximum gradient of O3 loss reaches 1.1 ppbv/100 km at surface level, while planetary boundary layer (PBL) column mean of O3 loss is more moderate, being approximately 0.7 ppbv/100 km. Relative high halogen can be found over Tibetan Plateau (TP) and the largest O3 loss (approximately 4-5 ppbv) in the PBL can be found between the western boundary of the domain and the TP. Halogens originating from marine sources can potentially affect O3 concentration transported from the stratosphere over the TP region. As part of efforts to improve our understanding of the effect of bromine and iodine chemistry on tropospheric O3, we call for more models and monitoring studies on halogen chemistry and be considered further in air pollution prevention and control policy.

Development and application of a hybrid long-short term memory - three dimensional variational technique for the improvement of PM2.5 forecasting.

The current state-of-the-art three-dimensional (3D) numerical model for air quality forecasting is restricted by the uncertainty from the emission inventory, physical/chemical parameterization, and meteorological prediction. Forecasting performance can be improved by using the 3D-variational (3D-VAR) technique for assimilating the observation data, which corrects the initial concentration field. However, errors from the prognostic model cause the correction effects at the first hour to be erased, and the bias of the forecast increases relatively fast as the simulation progresses. As an emerging alternative technique, long short-term memory (LSTM) shows promising performance in air quality forecasting for individual stations and outperforms the traditional persistent statistical models. In this study, a new method was developed to combine a 3D numerical model with 3D-VAR and LSTM techniques. This method integrates the advantage of LSTM, namely its high-accuracy forecasting for a single station and that of the 3D-VAR technique, namely its ability to extend improvement to the whole simulation domain. This hybrid method can effectively improve PM2.5 forecasting for the next 24 h, relative to forecasting with the 3D-VAR technique which uses the initial hour concentration correction. Results showed that the root-mean-square error and normalized mean error were decreased by 29.3% and 33.3% in the validation stations, respectively. The LSTM-3D-VAR method developed in this study can be further applied in other regions to improve the forecasting of PM2.5 and other ambient pollutants.

Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020.

Previous studies on long-term ozone (O3) variations in China have reported inconsistent conclusions on the role of meteorological factors in controlling said variations. In this study, we used an observation-based decomposition model to conduct an up-to-date investigation of the effects of meteorological factors on the variations in nitrogen dioxide (NO2) and O3 concentrations in China in the summer from 2013 to 2020. The variations in NO2 and O3 concentrations after removing the major meteorological effects were then analyzed to improve our understanding of O3 formation regimes. Ground measurements show that both NO2 and O3 concentrations decreased in eastern, central, and southeastern China (e.g., NO2 and O3 concentrations in Wuhan reduced by 4.3 and 6.2 ppb, respectively), which was not anticipated. Analyses of meteorological effects showed that reduced wind strength, decreased temperature, and increased relative humidity significantly reduced O3 concentrations in eastern and central China (e.g., by 10.5 ppb in Wuhan). After removing the major meteorological effects, the O3 trends were reversed in eastern and central China (e.g., increased by 4.9 ppb in Wuhan). The contrasting trends in NO2 and O3 concentrations suggest that their O3 formations were sensitive to volatile organic compounds (VOC-limited regime). In southeastern China, both NO2 and O3 concentrations decreased, implying that the O3 formation regimes changed to mixed sensitive or nitrogen oxide-limited (NOx-limited) regimes. The meteorological effects varied by region and may play a dominant role in controlling the long-term O3 variation. Our results indicate that the attribution of O3 variation to emission control without accounting for meteorological effects can be misleading.

Estimation of Health Effects and Economic Losses from Ambient Air Pollution in Undeveloped Areas: Evidence from Guangxi, China.

Guangxi Zhuang Autonomous Region, located in the southwest of China, has rapidly developed since the late 2000s. Similar to other regions, economic development has been accompanied by environmental problems, especially air pollution, which can adversely affect the health of residents in the area. In this study, we estimated the negative health effects of three major ambient pollutants, Particulate Matter with a diameter of 10 µm or less (PM10), Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2) in Guangxi from 2011 to 2016 using a log-linear exposure-response function. We monetarized the economic loss using the value of statistical life (VSL) and the cost of illness (COI) methods. The results show that the total possible short-term all-cause mortality values due to PM10, SO2, and NO2 were 28,396, with the confidence intervals from 14,664 to 42,014 (14,664-42,014), 24,618 (15,480-33,371), and 46,365 (31,158-61,423), respectively. The mortality from the three pollutants was 48,098 (19,972-75,973). The economic loss of the health burden from the three pollutants was 40,555 (24,172-57,585), which was 2.86% (1.70-4.06%) of the regional gross domestic product. The result of the comparative analysis among different cities showed that urbanization, industrialization, and residents' income are important factors in air-pollution-caused health damage and subsequent economic loss. We conclude that the health burden caused by ambient pollutants in developing regions, accompanied by its rapid socio-economic growth, is significant and tighter regulation is needed in the future to alleviate air pollution and mitigate the related health damage.

Application of air parcel residence time analysis for air pollution prevention and control policy in the Pearl River Delta region.

In this study, the concept of air parcel residence time was raised and the APRT was investigated to study its potential application in air pollution prevention and control in the Pearl River Delta (PRD) region. The APRT in the PRD region was defined as the total period for which an air parcel stays within the PRD region. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to calculate the hourly APRT in 2012, 2014, and 2015 based on forward trajectories from 16,720 starting locations. The seasonal APRT results revealed that long APRT was mainly distributed in southern PRD in the summer half year, but in northeastern PRD in the winter half year. This is related to the prevailing wind directions in the summer and winter monsoons. Moreover, the comparison of APRT in different years revealed that the dispersion condition was relatively poor in fall in 2012 and throughout 2014 but was relatively favorable in 2015, which also corresponded to the pollutant concentrations. The APRT calculated from regional air pollution days indicated that the emission reduction strategy should be implemented in the key areas, namely the eastern and central Guangzhou, western Huizou, and the border between Foshan and Jiangmen, and the construction of new factories should not be allowed in these areas. Compared to the APRT, which was investigated to trace the air pollution source, population exposure to air parcels (PEAP) was investigated to orient the influence of path-and-time-weighted sources to population. Consequently, a high PEAP was found to be distributed mainly in the central Guangzhou and Shenzhen and scattered in other urban areas.

Source and exposure apportionments of ambient PM2.5 under different synoptic patterns in the Pearl River Delta region.

PM2.5 is one of the most notorious ambient pollutants in the Pearl River Delta (PRD) region during episodic conditions. In this work, the Comprehensive Air Quality Model with extension (CAMx) was used together with the Particulate Source Apportionment Technology (PSAT) module to analyze the influences of different sources on PM2.5 concentration in the PRD region under different synoptic patterns (sea high pressure, sub-tropical high pressure and equalizing pressure field). The result shows that the PM2.5 concentration increases to different degrees under the three synoptic patterns. The emissions outside the PRD region contribute more than 54% under episodic conditions. The source category contribution varies little under different synoptic patterns. Area (46%), mobile (21%) and industry point source (16%) are the major contributors over the three episodic cases. The regional source contributions (from other cities within the PRD) to Foshan, Zhongshan and Zhaoqing are larger and can reach up to 33%. People living in the PRD region are more exposed to pollutants produced from the area and mobile sources. About 80% of the population is exposed to PM2.5 levels exceeding the IT-3 standard during the pollution episodes.

To what extent can the below-cloud washout effect influence the PM2.5? A combined observational and modeling study.

The below-cloud washout (BCW) effect on PM2.5 concentration during periods of rain is still a subject of debate. Existing BCW schemes for PM2.5 have large deficiencies that influence its simulation in 3D chemical transport models (CTMs). In this study, a 7-year dataset with high temporal resolution (in minutes) sampled from a pristine rural site is used to calculate the BCW coefficient during the rain events. The data used for the BCW coefficient calculation cover a wide range of rain intensity from 2 mm h-1 to 60 mm h-1. The BCW coefficient linearly correlates with the rain intensity, with a correlation coefficient of 0.82. The coefficient has a magnitude of 10-5 to 10-4 s-1 when the rain intensity ranges from 1 to 40 mm h-1. After implementing the updated BCW scheme into the Comprehensive Air Quality Model with Extensions (CAMx) model, the performance of PM2.5 simulation improves for the two months of heavy rain. Apart from the CAMx model, our scheme can be easily implemented into other 3D CTMs to improve PM2.5 simulation during rainy days. The BCW effect can clean around 10-40% of the PM2.5 over our study region, which can help to reduce the PM2.5 exposure level for residents, and the health burdens caused by this pollutant can thus be reduced. Rainmaking is a potential way to decrease PM2.5 concentration, but it cannot be the key method to reduce the PM2.5 level to the standard during episodic cases (e.g., >200 µg/m3).

A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak.

Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R2 values higher than 0.97, root mean square error less than 1.0 µg/m3, and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs.

Differences in concentration and source apportionment of PM2.5 between 2006 and 2015 over the PRD region in southern China.

During China's 11th Five Year Plan (FYP) and 12th FYP (2006-2015), a series of air pollution control measures was implemented in the Pearl River Delta (PRD) region. Therefore, it is vital to determine how the concentration and sources of fine particulate matter (PM2.5) in this region changed between 2006 and 2015. In this work, using 2006 and 2015 emission inventories, the concentration and source apportionment of PM2.5 were simulated using the Weather Research and Forecast - Comprehensive Air Quality Model with Extensions (WRF-CAMx) for January, April, July and October in the PRD region. The PM2.5 in 10 cities and the contributions made by sources in six major categories were tracked using the Particulate Source Apportionment Technology (PSAT) module. The results showed that the PM2.5 concentration was lower across the entire PRD region in the 2015 emission scenario than in the 2006 scenario, and that the degree of this reduction exceeded 40 µg/m3 in some places. The PM2.5 contributed by mobile emissions decreased the most, especially in Guangzhou, Foshan and Shenzhen, where mobile contributions decreased from 15.0, 17.9 and 13.0 µg/m3 in 2006 to 2.6, 3.1 and 4.1 µg/m3 in 2015, respectively. The PM2.5 contributed by power plants also decreased, and in Dongguan and Guangzhou, the extent of this reduction reached 2.5 and 3.4 µg/m3 respectively. However, due to an increase in industrial production and population size, the PM2.5 from industrial point sources and area sources also increased between 2006 and 2015 in some of the cities. Investigation of the source apportionment for city centers yielded similar results. In addition to emissions within the PRD region, outside-PRD non-local contribution is still an important PM2.5 contributor. Hence, more stringent policies for controlling industrial and area sources and deepening province-to-province cooperation are urgently needed as the next step in PM2.5 control.

Analysis of the adverse health effects of PM2.5 from 2001 to 2017 in China and the role of urbanization in aggravating the health burden.

In this study, the trend of PM2.5 concentrations and its adverse health effects in China from 2001 to 2017 are estimated utilizing 1-km high-resolution annual satellite-retrieved PM2.5 data. PM2.5 concentrations for most of the provinces/cities remained stable from 2001 to 2012; however, following the issue of the Air Pollution Prevention and Control Action Plan (APPCAP) by the central government of China, a dramatic decrease in PM2.5 concentrations from 2013 to 2017 occurred. Premature mortality caused by PM2.5 dropped from 1,078,800 in 2014 to 962,900 in 2017. The PM2.5 caused 17-year average mortality ranges from 3800 in Hainan Province to 124,800 in Henan Province. The health cost benefits gained by the reduction of PM2.5 pollution amounted to US $193,800 in 2017 (compared to the costs due to PM2.5 concentrations in 2013), amounting to 1.58% of the total national GDP. The impacts of urbanization on PM2.5 concentration and mortality are analyzed. The PM2.5 concentration and its induced mortality density in dense urban areas are much higher than those in rural areas. The aggravation of PM2.5 associated premature mortality in urban areas is mainly due to the larger amount of emissions and to urban migration, and 6500 deaths in 2014 could have been avoided were the population ratios in dense-urban/normal-urban/rural areas to be reversed to the ones in 2001. It is recommended that people with respiratory-related diseases live in rural areas, where the pollutant concentration is relatively low.

A novel approach to improve poly-γ-glutamic acid production by NADPH Regeneration in Bacillus licheniformis WX-02.

Poly-γ-glutamic acid (γ-PGA) is an important biochemical product with a variety of applications. This work reports a novel approach to improve γ-PGA through over expression of key enzymes in cofactor NADPH generating process for NADPH pool. Six genes encoding the key enzymes in NADPH generation were over-expressed in the γ-PGA producing strain B. licheniformis WX-02. Among various recombinants, the strain over-expressing zwf gene (coding for glucose-6-phosphate dehydrogenase), WX-zwf, produced the highest γ-PGA concentration (9.13 g/L), 35% improvement compared to the control strain WX-pHY300. However, the growth rates and glucose uptake rates of the mutant WX-zwf were decreased. The transcriptional levels of the genes pgsB and pgsC responsible for γ-PGA biosynthesis were increased by 8.21- and 5.26-fold, respectively. The Zwf activity of the zwf over expression strain increased by 9.28-fold, which led to the improvement of the NADPH generation, and decrease of accumulation of by-products acetoin and 2,3-butanediol. Collectively, these results demonstrated that NADPH generation via over-expression of Zwf is as an effective strategy to improve the γ-PGA production in B. licheniformis.

Assessment of health burden caused by particulate matter in southern China using high-resolution satellite observation.

As the major engine of economic growth in China, the Pearl River Delta (PRD) region is one of the most urbanized regions in the world. Rapid development has brought great wealth to its citizens; however, at the same time, increasing emissions of ambient pollutants from vehicles and industrial combustions have caused considerable air pollution and negative health effects for the region's residents. In this study, the concentration response function method was applied together with satellite-retrieved particulate matter (PM10 and PM2.5) concentration data to estimate the health burden caused by this pollutant from 2004 to 2013. The value of statistical life was used to calculate the economic loss due to the negative health effects of particulate matter pollution. Our results show that in the whole PRD region, the estimated number of deaths from the four diseases attributable to PM2.5 was the highest in 2012, at 45,000 (19,000-61,000); the number of all-cause hospital admissions due to PM10 was the highest in 2013, reaching up to 91,000 (0-270,000) (excluding Hong Kong). Among the 10 cities, the capital city Guangzhou suffered the most from ambient particulate matter pollution and had the highest mortality and morbidity over the 10years. The cost of mortality in this region was the highest in 2012, at 46,000 million USD, or around 6.1% of local total gross domestic product (GDP). The positive spatial relationship between the degree of urbanization and the particulate matter concentration proves that the urbanization process does worsen air quality and hence increases the health risks of local urban citizens. It is recommended that local governments further enhance their control policies to better guarantee the health and wealth benefits of local residents.

Estimation of health and economic costs of air pollution over the Pearl River Delta region in China.

The Pearl River Delta region (PRD) is the economic growth engine of China and also one of the most urbanized regions in the world. As a two-sided sword, rapid economic development causes air pollution and poses adverse health effects to the citizens in this area. This work estimated the negative health effects in the PRD caused by the four major ambient pollutants (SO2, NO2, O3 and PM10) from 2010 to 2013 by using a log linear exposure-response function and the WRF-CMAQ modeling system. Economic loss due to mortality and morbidity was evaluated by the value of statistical life (VSL) and cost of illness (COI) methods. The results show that the overall possible short-term all-cause mortality due to NO2, O3 and PM10 reached the highest in 2013 with the values being 13,217-22,800. The highest total economic loss, which ranged from 14,768 to 25,305million USD, occurred in 2013 and was equivalent to 1.4%-2.3% of the local gross domestic product. The monthly profile of cases of negative health effects varied by city and the types of ambient pollutants. The ratio of mortality attributed to air pollutants to total population was higher in urban areas than in rural areas. People living in the countryside should consider the possible adverse health effects of urban areas before they plan a move to the city. The results show that the health burden caused by the ambient pollutants over this region is serious and suggest that tighter control policies should be implemented in the future to reduce the level of air pollution.