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Regional meteorological conditions and emissions reduction are closely related to air quality. China has a monsoonal climate and regional meteorological conditions are significantly impacted by interannual climate variability. The objective of this study was to evaluate the contributions of meteorological conditions and emissions reduction to regional improvements in air quality. Trend analyses of key meteorological factors and air pollution for the Beijing-Tianjin-Hebei region, Chengdu-Chongqing region, Yangtze River Delta, and Pearl River Delta urban agglomeration areas were performed for the period from 2001 to 2018, and K-Nearest Neighbor (KNN) models were constructed for each calendar year. The analysis showed that approximately half of the years between 2001 and 2018 experienced abnormal global-scale climate conditions, i.e., El Niño and La Niña. Both emissions reduction and climate changes contributed to the improvement of air quality during the study period. The contribution of meteorological conditions to air quality improvement under abnormal climate conditions was 51% compared to 30% under normal climate conditions in the Beijing-Tianjin-Hebei region; for the Yangtze River Delta and Pearl River Delta regions, meteorological conditions contributed approximately 50% to the improvement of air quality under both abnormal and normal climate conditions. In addition, the contribution of emissions reduction to air quality improvement was higher in the study areas during 2015-2018 compared to 2001-2012. This indicates that emissions reduction has played an increasingly important role in air quality improvements largely due to the implementation of a variety of emission control measures. However, the contribution of meteorological conditions to air quality improvement cannot be ignored.

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Non-road equipment is one of the key contributing sources to air pollution. Thus, an accurate development of emission inventory from non-road equipment is imperative for air quality management, especially for equipment with a large population such as diesel-fueled forklifts. The objective of this paper is to characterize duty-cycle based emissions from diesel-fueled forklifts using a portable emission measurement system (PEMS). Three duty-cycles were defined in this study, including idling, moving, and working (active duty operation) and used to characterize in-use emissions for diesel-fueled forklifts. A total of twelve diesel-fueled forklifts were selected for real-world emission measurements. Results showed that fuel-based emission factors appear to have smaller variability compared to time-based ones. For example, the time-based emission factors for CO, HC, NO, and PM2.5 for forklifts were estimated to be 16.6-43.9, 5.3-15.1, 26.2-49.9, 5.5-11.1 g/hr with the fuel-based emission factors being 12.1-20.3, 4.1-8.3, 19.1-32.4, 3.5-6.5 g/kg-fuel, respectively. NO emissions appear to be the biggest concern for emissions control. Furthermore, most of the emissions factors estimated from this study are significantly different from those in both National Guideline for Emission Inventory Development for Non-Road Equipment in China and well-developed emission factor models such as NONROAD by US EPA. This implies that localized, preferably fuel-based emission factors should be adjusted based on real-world emission measurements in order to develop a representative emission inventory for non-road equipment.

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As important components of PM2.5, metal elements are extremely harmful to people and also have source specificity. Understanding the characteristics of PM2.5 metal pollution in the two different types of cities can help adjust the layout of regional industrial structure and improve the environment. PM2.5 samples during haze/non-haze periods were collected in Chengdu City and Renshou County. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine the mass concentrations of eighteen metal elements in collected PM2.5 samples. The positive matrix factorization (PMF) model was used for source apportionment analysis for metal elements in PM2.5. The analysis showed that the ratio of trace elements from fugitive dust, motor vehicle emissions, and coal burning to the total elements is greater in Chengdu City than that in Renshou County. The proportion of trace elements from biomass combustion, industrial, and fuel sources in Renshou County is higher than that in Chengdu City. In addition, concentrations of Cd, As, and Cr in both areas exceeded the standards, indicating the occurrences of heavy metal pollution. During the haze period, the total concentrations of compositional metal elements in PM2.5 increased, although the rate was much lower than that for PM2.5. The ratios of elements between haze and non-haze periods ranged from 0.7 (Al) to 2.8 (Ba) in Chengdu City, and from 0.8 (Al) to 3.1 (Mn) in Renshou County. Among all metal elements, the increase rate for trace elements from coal burning and industrial activities was relatively large but small for those from fugitive dust, with the growth in trace elements from motor vehicles being modest. The results of this study indicated that the characteristics of pollution and source of metal elements in PM2.5 varied by economic scale, development mode, and industrial layout. In large cities such as Chengdu City, where economic development is mainly focused on tertiary industry, air pollution is mainly caused by transportation and urban construction, while in suburban area such as Renshou County, where secondary or heavy industry are the focus for economic development, the pollution is mainly affected by energy consumption and industrial production.

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Emissions from non-road equipment are attracting more attention due to their increasing contribution to air pollution. Thus, an accurate estimate of emission inventory for non-road equipment is imperative for air quality management and improvement. Activity data from a large range of construction equipment were collected from multiple sources, including on-site/phone interviews and literature review, and used for further analysis to characterize its operations. Activity analysis of construction equipment included:①activity of construction equipment by type (functionality); ②activity differences by geographical area; and ③activity differences by age. A back-propagation neural network model was developed to estimate the construction equipment population in China from 2018 to 2025. Furthermore, real-world measurements of emissions were made on 47 selected examples of construction equipment using a portable emission measurement system. Population, activity, and emission factors were then combined to develop emission inventories for construction equipment in China from 2015 to 2025. The results showed that activity of construction equipment differs by type or functionality, ranging from 1439 to 4332 hours per year. Furthermore, there are differences in activity by as much as three times due to geographical area differences for the same construction equipment type. In general, activity of construction equipment decreases as it ages by a rate of approximately 140 to 150 hours per year. It is estimated that CO, HC, NO, and PM2.5 emissions of construction equipment in China in 2015 were approximately 2.099, 0.462, 3.452, and 0.574 million tons, respectively. Compared to 2015, due to the slow growth of the construction equipment population, CO, HC, and PM2.5 emissions will decrease by 2.4%-33.1% and 7.1%-64.7% by 2020 and 2025, respectively, depending on pollutant. It should be noted that NO emissions appear to increase slightly for the first several years in the future, but then decrease after 2020. As increasingly stringent regulations have been enforced for on-road vehicles, but less has been done regarding non-road equipment, although total emissions from non-road equipment continue to decrease, their contribution to air pollution will continue to increase; they should therefore be one of the focuses for future work.

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To compare the pollution characteristics of carbonaceous aerosol components in the atmosphere between urban and suburban areas, Chengdu City and Renshou County were selected as study areas from which 88 samples of PM2.5 during haze and non-haze periods were collected and analyzed. Quantification of mass concentrations of PM2.5, carbonaceous aerosol components[organic carbon (OC), elemental carbon (EC), and secondary organic carbon (SOC)], along with correlation analysis of OC and EC, and principal component analysis (PCA) of carbon components were carried out. The results show that pollutant concentrations during the haze period were higher than those during the non-haze period. The OC and EC for Chengdu City and Renshou County were positively correlated, with their correlation coefficients during the non-haze period higher than those during the haze period. The ratios of SOC/PM2.5 in Renshou County were higher than those in Chengdu City during the haze period. This indicates that secondary aerosols play a more important role in haze formation in Renshou than in Chengdu City. In contrast, the proportion of secondary aerosols during the non-haze period in Chengdu City was significantly higher, indicating that direct emissions are still the main cause of air pollution in Chengdu City. PCA results showed that PM2.5 formation in both Chengdu City and Renshou County was mainly due to coal burning, vehicle operation, and biomass burning.

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Vehicle emissions have become an increasingly important source to air pollution in China, thus an accurate estimate of vehicle emissions is essential but challenging for policy-making toward air quality improvement. Since vehicle emissions are episodic, roadway-based micro/meso-scale emissions are getting more and more attention for roadway exposure assessment and accuracy improvement of emission inventory. Hence, it is necessary to characterize the temporal and spatial distribution of vehicle emissions. However, due to the large number of vehicle population and managerial difficulties, it might not be practical to develop vehicle emission inventory based on all individual vehicles at a city level. This study aimed to develop an approach to use web-based real-time traffic data to estimate meso-scale vehicle emissions at a city level. Taking Chengdu as an example, traffic characteristics include driving modes, traffic flows, and fleet compositions under different traffic conditions were quantified using real-world measurements. Web-based traffic data was shown to have adequate accuracy for traffic characterization and thus emission estimation. Real-time traffic conditions of the study area derived from web-based traffic data were then matched with corresponding traffic characteristics. Combining with vehicle modal emission rates, roadway-based vehicle emissions were quantified both spatially and temporally. As expected, estimated roadway-based emissions correlated well with traffic conditions both temporally and spatially. Heavier traffic is usually associated with higher emissions. This study demonstrated that the web-based traffic data can be used in transportation and environment related research. Findings from this work can be used for hotspot identification in traffic and emissions and the associated risk analysis, traffic management, and many other applications.

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In this paper, the objective is to characterize real-world tailpipe emissions for excavators. Eight excavators in several construction sites in Chengdu were selected in this study. A portable emission measurement system (PEMS) was used for real-world emissions measurements (i. e., CO, HC, NO, and PM2.5) for three predefined operation modes:idling, moving, and working. The results showed that the tailpipe emissions of excavators vary depending on the operation mode as well as the equipment. NO emissions were relatively stable when the engine was idling compared to when the excavator was moving or doing actual work. In addition, excavators that complied to different emissions standards also exhibited different emissions, with those that met higher emission standards producing fewer emissions. For example, when comparing excavators complying with Stage Ⅱ emission standards to those complying with Stage Ⅰ emission standards, the NO and PM2.5 emissions appeared to decrease. On average, the NO emissions decreased by 8%, 35%, and 5%, and the PM2.5 emissions decreased by 88%, 87%, and 80% for the idling, moving, and working modes, respectively. Furthermore, the studies showed significant differences existed between the emissions factors in the real-world measurements and those recommended by national guidance. This indicated that real-world emission measurements of non-road equipment will play a key role in emissions inventory development. This study demonstrated that PEMS can be used to characterize real-world emissions from non-road equipment.

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Nitrogen dioxide (NO2) significantly contributes to air pollution. Long-term NO2 exposure is harmful to human health. The NO2 pollution in China has surpassed developed countries and attracts international attention. To understand the spatial and temporal distributions of NO2 across Chengdu in Southwest China, a random forest (RF) model was developed based on NO2 environmental monitoring data, the Ozone Monitoring Instrument (OMI) satellite retrievals, and geographic covariates. The RF model showed good performance with a cross validation R2 of 0.77, and a root mean square error (RMSE) of 11.0 µg/m3. The ground-level NO2 concentrations of Chengdu for 2005-2016 were predicted using the developed model with the multiyear population weighted NO2 concentration being 41.7 ±â€¯11.7 µg/m3. The predicted NO2 concentrations exhibited a clear seasonal variation trend with winter being the highest and summer being the lowest. Furthermore, higher NO2 concentrations in the downtown areas were observed than that in the rural areas indicating the former being attributed to more anthropogenic sources. The population weighted NO2 concentrations with deseasonlization were relatively high during 2011-2013. The NO2 concentration increased at a rate of 0.81 µg/m3/year before 2011 (43.4 ±â€¯11.2 µg/m3) and decreased at a rate of -1.03 µg/m3/year after 2013 (44.8 ±â€¯12.8 µg/m3).

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A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO2 concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R2 = 0.62 (RMSE = 13.3 µg/m3) for daily and R2 = 0.73 (RMSE = 6.5 µg/m3) for spatial predictions. The nationwide population-weighted multiyear average of NO2 was predicted to be 30.9 ± 11.7 µg/m3 (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 µg/m3/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 µg/m3/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO2 was predicted to be the highest in North China (40.3 ± 10.3 µg/m3) and lowest in Southwest China (24.9 ± 9.4 µg/m3). Approximately 25% of the population lived in nonattainment areas with annual-average NO2 > 40 µg/m3. A piecewise linear function with an abrupt point around 100 people/km2 characterized the relationship between the population density and the NO2, indicating a threshold of aggravated NO2 pollution due to urbanization. Leveraging the ground-level NO2 observations, this study fills the gap of statistically modeling nationwide NO2 in China, and provides essential data for epidemiological research and air quality management.

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The objective of this paper is to develop and demonstrate a fuel-based approach for emissions factor estimation for highway paving construction equipment in China for better accuracy. A highway construction site in Chengdu was selected for this study with NO emissions being characterized and demonstrated. Four commonly used paving equipment, i.e., three rollers and one paver were selected in this study. A portable emission measurement system (PEMS) was developed and used for emission measurements of selected equipment during real-world highway construction duties. Three duty modes were defined to characterize the NO emissions, i.e., idling, moving, and working. In order to develop a representative emission factor for these highway construction equipment, composite emission factors were estimated using modal emission rates and the corresponding modal durations in the process of typical construction duties. Depending on duty mode and equipment type, NO emission rate ranged from 2.6-63.7mg/s and 6.0-55.6g/kg-fuel with the fuel consumption ranging from 0.31-4.52 g/s correspondingly. The NO composite emission factor was estimated to be 9-41mg/s with the single-drum roller being the highest and double-drum roller being the lowest and 6-30g/kg-fuel with the pneumatic tire roller being the highest while the double-drum roller being the lowest. For the paver, both time-based and fuel consumption-based NO composite emission rates are higher than all of the rollers with 56mg/s and 30g/kg-fuel, respectively. In terms of time-based quantity, the working mode contributes more than the other modes with idling being the least for both emissions and fuel consumption. In contrast, the fuel-based emission rate appears to have less variability in emissions. Thus, in order to estimate emission factors for emission inventory development, the fuel-based emission factor may be selected for better accuracy. IMPLICATIONS: The fuel-based composite emissions factors will be less variable and more accurate than time-based emission factors. As a consequence, emissions inventory developed using this approach will be more accurate and practical.

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Vehicle-specific microscale fuel use and emissions rate models are developed based upon real-world hot-stabilized tailpipe measurements made using a portable emissions measurement system. Consecutive averaging periods of one to three multiples of the response time are used to compare two semiempirical physically based modeling schemes. One scheme is based on internally observable variables (IOVs), such as engine speed and manifold absolute pressure, while the other is based on externally observable variables (EOVs), such as speed, acceleration, and road grade. For NO, HC, and CO emission rates, the average R(2) ranged from 0.41 to 0.66 for the former and from 0.17 to 0.30 for the latter. The EOV models have R(2) for CO(2) of 0.43 to 0.79 versus 0.99 for the IOV models. The models are sensitive to episodic events in driving cycles such as high acceleration. Intervehicle and fleet average modeling approaches are compared; the former account for microscale variations that might be useful for some types of assessments. EOV-based models have practical value for traffic management or simulation applications since IOVs usually are not available or not used for emission estimation.

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The objective here is to quantify the variability in emissions of selected light duty gasoline vehicles by routes, time of day, road grade, and vehicle with a focus on the impact of routes and road grade. Field experiments using a portable emission measurement system were conducted under real-world driving cycles. The study area included two origin/destination pairs, each with three alternative routes. Total emissions varied from trip to trip and from route to route due to variations in average speed and travel time. On an average trip basis, the total NO emissions differed by 24% when comparing alternative routes and by 19% when comparing congested travel time with less congested traffic time. Positive road grades were associated with an approximately 20% increase in localized emissions rates, while negative road grades were associated with a similar relative decrease. The average vehicle-specific power based NO modal emission rates differed by more than 2 orders of magnitude when comparing different vehicles. The results demonstrate that alternative routing can significantly impact trip emissions. Furthermore, road grade should be taken into account for localized emissions estimation. Vehicle-specific models are needed to capture episodic effects of emissions for near-road short-term human exposure assessment.

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The objective of this paper is to quantify and evaluate the effects of response time of a portable emission measurement system (PEMS). The PEMS measures tailpipe emissions and vehicle dynamics on a second-by-second basis. Response times of the PEMS for exhaust concentrations were quantified on the basis of fixed periods of measurement of calibration gases for NO, hydrocarbons (HC), CO, and CO2. The time constant was quantified on the basis of the time to reach 63% of the maximum measured value when calibration gas was continuously administered for a period of typically 20 s or more. The time constant was found to be 6 s for NO and 3 s each for CO, HC, and CO2. Measurement errors associated with the response time of the PEMS were quantified. A first-order dynamic discrete model was developed to simulate the instrument measurements. Simulations showed that correction improves the measurement accuracy. Correction with smoothing better improves the measurement accuracy, especially when the noise is relatively large. On a trip level, the average error of the simulated measurements relative to the simulated signal before correction is -4%, which is deemed to be acceptable. For real-world data, smoothing and correction is recommended for major peaks to improve the measurement accuracy.

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An actinomycete strain, AM105(T), that produces rifamycin, was isolated from mangrove sediment samples collected from the South China Sea. The strain showed closest 16S rRNA gene sequence similarity to Micromonospora matsumotoense (98.0%). Chemotaxonomic characteristics of the isolate coincided with members of the genus Micromonospora. The value of DNA-DNA relatedness to M. matsumotoense (53.6%) and phenotypic differences from phylogenetically related Micromonospora species indicated that this isolate belongs to a novel species, for which the name Micromonospora rifamycinica sp. nov. is proposed. The type strain is AM105(T) (=CGMCC 4.2495(T)=DSM 44983(T)).

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Vehicle-specific power (VSP) is useful for explaining a substantial portion of variability in real-world vehicle emissions, such as those measured with portable emissions monitoring systems (PEMS). VSP is a function of vehicle speed, acceleration, and road grade. Road grade is shown to significantly affect estimates of both VSP and of real-world emissions via sensitivity analysis and analysis of empirical data. However, road grade is difficult to measure reliably using PEMS. Therefore, alternative methods for estimating road grade were identified and compared. A preferred method for estimating road grade was explored in more detail based on light detection and ranging (LIDAR) data. The method includes buffering LIDAR data onto roadway maps using a geographic information system tool, defining segments of roadway based on criteria pertaining to vertical curvature, quantification of roadway elevations within the buffered segments, and estimation of road grade and banking by fitting a plane to each segment. Factors influencing errors in road grade estimates are discussed. The method was evaluated by application to selected interstate highways and comparison to design drawing data. The development and application of LIDAR-based road grade data are demonstrated via a case study using PEMS data collected in the Research Triangle Park, NC, area. LIDAR data are shown to be reliable and accurate for road grade estimation for vehicle emissions modeling.

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With culture-independent approach, microbial total DNA was directly extracted from Pachychalina sp. Using the total microbial DNA as template, archaeal 16S rDNAs were amplified by PCR with universal primers. Amplified products were cloned into T-vector and secondarily amplified by PCR. Then the secondarily amplified products were purified to be further characterized by termed ARDRA (amplified rDNA restriction analysis). According to the enzyme restriction mapping, the apparent difference among them were disclosed. Furthermore eight archaeal cloned partial sequences were acquired and built up a phylogenetic tree. In the phylogenetic tree, the eight archaea belonged to Methanogenium organophilum and Methanoplanus petrolearius, but the 16S rDNAs similarities among them and those archaea registered in RDP Database didn't excess to 90%. It means that they maybe represent some novel archaeal groups.

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