Characterization of VOC emissions from construction machinery and river ships in the Pearl River Delta of China

Pollution characteristics, source appointment and environmental effect of oxygenated volatile organic compounds in Guangdong-Hong Kong-Macao Greater Bay Area: Implication for air quality management

Same as other bay areas, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is also suffering atmospheric composite pollution. Even a series of atmospheric environment management policies have been conducted to win the "blue sky defense battle", the atmospheric secondary pollutants (e.g., O3) originated from oxygenated volatile organic compounds (OVOCs) still threaten the air quality in GBA. However, there lacks a systematic summary on the emission, formation, pollution and environmental effects of OVOCs in this region for further air quality management. This review focused on the researches related to OVOCs in GBA, including their pollution characteristics, detection methods, source distributions, secondary formations, and impacts on the atmosphere. Pollution profile of OVOCs in GBA revealed that the concentration percentage among total VOCs from Guangzhou and Dongguan cities exceeded 50 %, while methanol, formaldehyde, acetone, and acetaldehyde were the top four highest concentrated OVOCs. The detection technique on regional atmospheric OVOCs (e.g., oxygenated organic molecules (OOMs)) underwent an evolution of off-line derivatization method, on-line spectroscopic method and on-line mass spectrometry method. The OVOCs in GBA were mainly from primary emissions (up to 80 %), including vehicle emissions and biomass combustion. The anthropogenic alkenes and aromatics in urban area, and natural isoprene in rural area also made a significant contribution to the secondary emission (e.g., photochemical formation) of OVOCs. About 20 % in average of ROx radicals was produced from photolysis of formaldehyde in comparison with O3, nitrous acid and rest OVOCs, while the reaction between OVOCs and free radical accelerated the NOx-O3 cycle, contributing to 15 %-60 % cumulative formation of O3 in GBA. Besides, the heterogeneous reactions of dicarbonyls generated 21 %-53 % of SOA. This review also provided suggestions for future research on OVOCs in terms of regional observation, analytical method and mechanistic study to support the development of a control and management strategy on OVOCs in GBA and China.

Characteristics of Volatile Organic Compounds Emitted from Airport Sources and Their Effects on Ozone Production

In recent years, commercial air transport has increased considerably. However, the compositions and source profiles of volatile organic compounds (VOCs) emitted from aircraft are still not clear. In this study, the characteristics of VOCs (including oxygenated VOCs (OVOCs)) emitted from airport sources were measured at Shenzhen Bao'an International Airport. The results showed that the compositions and proportions of VOC species showed significant differences as the aircraft operating state changed. OVOCs were the dominant species and accounted for 63.17%, 58.44%, and 51.60% of the total VOC mass concentration during the taxiing, approach, and take-off stages. Propionaldehyde and acetone were the main OVOCs, and dichloromethane and 1,2-dichloroethane were the main halohydrocarbons. Propane had the highest proportion among all alkanes, while toluene and benzene were the predominant aromatic hydrocarbons. Compared with the source profiles of VOCs from construction machinery, the proportions of halogenated hydrocarbons and alkanes emitted from aircraft were significantly higher, as were those of propionaldehyde and acetone. OVOCs were still the dominant VOC species in aircraft emissions, and their calculated ozone formation potential (OFP) was much higher than that of other VOC species at all stages of aircraft operations. Acetone, propionaldehyde, formaldehyde, acetaldehyde, and ethylene were the greatest contributors to ozone production. This study comprehensively measured the distribution characteristics of VOCs, and its results will aid in the construction of a source profile inventory of VOCs emitted from aircraft sources in real atmospheric environments.

Emission inventory and air quality impact of non-road construction equipment in different emission stages

Non-road construction equipment (NRCE) is an important source of air pollution, and it is crucial to fully understand the impact of NRCE on atmospheric PM2.5 and O3 pollution. However, systematic assessment of the impact of NRCE emissions on the atmosphere is lacking, especially with the latest implementation of the Stage IV Standard, and current research progress is insufficient for the development of effective control measures. This study estimated NRCE emission inventories at different emission standard stages and their impact on the atmosphere, using the "2 + 26" cities as the case study area. The results showed that the total NRCE emissions of CO, NOx, VOC, and PM2.5 were 387, 418, 82, and 24 kt in 2015 and 319, 262, 62, and 15 kt in 2020 and are predicted to be 270, 226, 48, and 10 kt in 2025, respectively. Simulation results showed that the contributions of NRCE to NO3-, NO2, PM2.5, and O3 were 16.7 %, 18.9 %, 7.7 %, and 8.2 % in 2015 to 13.6 %, 18.4 %, 6.5 %, and 6.7 % in 2020, respectively. In both 2015 and 2020, NRCE emissions in southern cities showed greater impacts on the average concentrations in the "2 + 26" cities than those in northern cities. The contributions of local NRCE emissions to local PM2.5 and O3 concentrations in the 28 cities ranged from 30 %-59 % and 13 %-39 %, respectively. The O3 sensitivity estimated by the HDDM illustrated that nonlinear characteristics highlighted the importance of coordinated control of NOx and VOC and can inspire development of post-processing technology and electricity substitution. The belt-like area connecting Zhengzhou to Beijing showed higher exposure concentrations of PM2.5 and O3, and the concentration exposure in urban areas was much higher than that in the rural and other areas. The environmental impact assessment of NRCE emissions can provide guidance for its management and development.

VOC emitted by biopharmaceutical industries: Source profiles, health risks, and secondary pollution

The biopharmaceutical industry contributes substantially to volatile organic compounds (VOCs) emissions, causing growing concerns and social developmental conflicts. This study conducted an on-site investigation of the process-based emission of VOCs from three biopharmaceutical enterprises. In the workshops of the three enterprises, 26 VOCs were detected, which could be sorted into 4 classes: hydrocarbons, aromatic hydrocarbons, oxygen-containing compounds, and nitrogen-containing compounds. Ketones were the main components of waste gases, accounting for 44.13%-77.85% of the overall VOCs. Process-based source profiles were compiled for each process unit, with the fermentation and extraction units of tiamulin fumarate being the main source of VOC emissions. Dimethyl heptanone, vinyl acetate, diethylamine, propylene glycol methyl ether (PGME), and benzene were screened as priority pollutants through a fuzzy comprehensive evaluation system. Ground level concentration simulation results of the Gauss plume diffusion model demonstrated that the diffusivity of VOCs in the atmosphere was relatively high, indicating potential non-carcinogenic and carcinogenic risks 1.5-2 km downwind. Furthermore, the process-based formation potentials of ozone and secondary organic aerosols (SOAs) were determined and indicated that N-methyl-2-pyrrolidone, dimethyl heptanone, and PGME should be preferentially controlled to reduce the ozone formation potential, whereas the control of benzene and chlorobenzene should be prioritized to reduce the generation of SOAs. Our results provide a basis for understanding the characteristics of VOC emission by biopharmaceutical industries and their diffusion, potentially allowing the development of measures to reduce health risks and secondary pollution.

Multi-component emission characteristics and high-resolution emission inventory of non-road construction equipment (NRCE) in China

With the continuous abatement of industries and vehicles in the past years in China, the comprehensive understanding and scientific control of non-road construction equipment (NRCE) may play an important role in alleviating PM_2.5 and O₃ pollution in the next stage. In this study, the emission rates of CO, HC, NOx, PM_2.5, CO₂ and the component profiles of HC and PM_2.5 from 3 loaders, 8 excavators and 4 forklifts under different operating conditions were tested for a systematic representation of NRCE emission characteristics. With the fusion of field tests, construction land types and population distributions, the NRCE emission inventory with a 0.1° × 0.1° resolution in nationwide and with a 0.01° × 0.01° resolution in Beijing-Tianjin-Hebei region (BTH) were established. The sample testing results suggested prominent differences in instantaneous emission rates and the composition characteristics among different equipment and under different operating modes. Generally, for NRCE, the dominant components are OC and EC for PM_2.5, and HC and olefin for OVOC. Especially, the proportion of olefins in idling mode is much higher than that in working mode. The measurement-based emission factors of various equipment exceeded the Stage III standard to varying degrees. The high-resolution emission inventory suggested that highly developed central and eastern areas, represented by BTH, showed the most prominent emissions in China. This study is a systematic representations of China's NRCE emissions, and the NRCE emission inventory establishment method with multiple data fusion has important methodological reference value for other emission sources.

Investigating the nonlinear and non-stationary relationship between PM2.5 and air pollutants by wavelet signal analysis in central Taiwan

In recent years, PM_2.5 has become a critical factor as an environmental indicator, causing severe air pollution that has negatively impacted nature and human health. This study used hourly data gathered in central Taiwan from 2015 to 2019 and applied spatiotemporal data analysis and wavelet analysis methods to investigate the cross-correlation between PM_2.5 and other air pollutants. Furthermore, it explored the correlation differences between adjacent stations after excluding major environmental factors such as climate and terrain. Wavelet coherence shows that PM_2.5 and air pollutants mostly have a significant correlation at the half-day and one-day frequencies, while the differences between PM_2.5 and PM₁₀ are only particle size; hence, not only is the correlation the most consistent among all air pollutants but also the lag time is the most negligible. Carbon monoxide (CO) is the primary source pollutant of PM_2.5 as it is also significantly correlated with PM_2.5 at most timescales. Sulfur dioxide (SO₂) and nitrogen oxide (NO_x) are related to the generation of secondary aerosols, which are important components of PM_2.5; therefore, the consistency of significant correlations improves as the timescale increases and the lag time becomes amplified. The pollution source mechanism of ozone (O₃) and PM_2.5 is not identical, so the correlation is lower than for other air pollutants; the lag time is also obviously influenced by the season changes that have significant fluctuations. At stations near the ocean such as Xianxi station and Shulu station, PM_2.5 and PM₁₀ have a higher correlation in the 24-h frequency, while the SO₂ and PM_2.5 at Sanyi station and Fengyuan station, which are close to industrial areas, have significant correlations in the 24-h frequency. This study hopes to help better understand the impact mechanisms behind different pollutants, and thus construct a better reference for establishing a complete air pollution prediction model in the future.

Exhaust emission inventory of typical construction machinery and its contribution to atmospheric pollutants in Chengdu, China

To study the emission characteristics of typical construction machinery in Chengdu, 12 construction machinery (excavators, bulldozers, loaders, and forklifts) under idling mode, moving mode, and working mode, were tested using a portable emission measurement system (PEMS). Under three operating modes, the typical construction machinery in the working mode was higher in the fuel-based average emission factors of PM_2.5 and NO_x, while the fuel-based average emission factors of HC and CO were higher in idling mode. Integrated the results of investigation on ownership and activity levels of construction machinery, an exhaust emission inventory of typical construction machinery of Chengdu in 2018 was established according to the recommendation method. The annual emission of PM_2.5, NO_x, HC, and CO were 1.67 × 10⁶, 1.61 × 10⁸, 3.83 × 10⁶, and 1.26 × 10⁷ kg, respectively, and the excavator contributed the maximum emissions, accounting for an average proportion of 43.95%. The emission of construction machinery in Chengdu exhibited a clear monthly trend, with the highest from April to October and the lowest from November to March. In addition, the exhaust emissions presented an obvious spot-like characteristics, and the high-value areas were mainly concentrated in the surrounding suburban counties such as Shuangliu Wenjiang etc. To reduce pollution from construction machinery and improve the quality of the atmospheric environment, more effective measures on housing construction and municipal construction should be taken in those districts in Chengdu.

A review and evaluation of nonroad diesel mobile machinery emission control in China

China's emission control for nonroad diesel mobile machinery (NDMM) must deal with a fast increase in stock as well as regulations that are two decades behind those for on-road vehicles. This study provides the first large-scale review and evaluation of China's NDMM policies, along with emission measurements and an investigation on diesel fuel quality. The sulfur contents of the investigated diesel declined from 430 ppm (median value) in 2011 to 6-8 ppm during the 2017-2018 period. The emission control of NO_x and PM greatly improved with the shift from the China II to China IV standards, as demonstrated by engine tests and field NO_x measurements. However, the NO_x emission factors for non-type-approved engines were approximately twice the limits of the China II standards. Emission compliance based on bench tests was not sufficient to control actual emissions because the field-measured NO_x emission factors of all machinery ranged from 24% to 225% greater than the respective emission limits for the engines. These circumstances adversely affected the effectiveness of the regulations and policies for China's emission control of NDMM. Nevertheless, the policies on new and in-use NDMM, as well as diesel fuel quality, prevented NO_x and PM emissions amounting to 4.4 Tg and 297.8 Gg during the period 2008-2017, respectively. The emission management strategy contributed to enhancing the international competitiveness of China's NDMM industries by promoting advanced technologies. For effective NDMM emission control in the future, portable testing and noncontact remote supervision should be strengthened; also, the issue of noncompliant diesel should be addressed through rigorous control measures and financial penalties.

Pollution Characteristics and Health Risk Assessment of VOCs in Jinghong

In order to investigate the seasonal variation in chemical characteristics of VOCs in the urban and suburban areas of southwest China, we used SUMMA canister sampling in Jinghong city from October 2016 to June 2017. Forty-eight VOC species concentrations were analyzed using atmospheric preconcentration gas chromatography–mass spectrometry (GC–MS), Then, regional VOC pollution characteristics, ozone formation potentials (OFP), source identity, and health risk assessments were studied. The results showed that the average concentration of total mass was 144.34 μg·m−3 in the urban area and 47.81 μg·m−3 in the suburban area. Alkanes accounted for the highest proportion of VOC groups at 38.11%, followed by olefins (36.60%) and aromatic hydrocarbons (25.28%). Propane and isoprene were the species with the highest mass concentrations in urban and suburban sampling sites. The calculation of OFP showed that the contributions of olefins and aromatic hydrocarbons were higher than those of alkanes. Through the ratio of specific species, the VOCs were mainly affected by motor vehicle exhaust emissions, fuel volatilization, vegetation emissions, and biomass combustion. Combined with the analysis of the backward trajectory model, biomass burning activities in Myanmar influenced the concentration of VOCs in Jinghong. Health risk assessments have shown that the noncarcinogenic risk and hazard index of atmospheric VOCs in Jinghong were low (less than 1). However, the value of the benzene cancer risk to the human body was higher than the safety threshold of 1 × 10−6, showing that benzene has carcinogenic risk. This study provides effective support for local governments formulating air pollution control policies.

Assessing a Fossil Fuels Externality with a New Neural Networks and Image Optimization Algorithm: The Case of Atmospheric Pollutants as Cofounders to COVID-19 Lethality

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM_2.5, PM₁₀ and CO₂) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM_2.5, PM₁₀ and CO₂ which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

Variability in real-world emissions and fuel consumption by diesel construction vehicles and policy implications

Strategically reducing the emission of non-road mobile source especially diesel construction vehicle (DCV) has a large potential in improving air quality and has attracted much scientific and public attention in recent years around the world. In this study, we explored real-world fuel consumption rate and gaseous emissions factors for multiple pollutants of three typical DCVs in China. The sampling campaign considered the operation mode, cumulative operation hour, emission standard stage and engine power. Results show that the accumulated fuel consumption per hour of vehicle weight for working, load-free moving and idling modes was 0.3, 0.2 and 0.1 kg/h·tons, respectively. The fuel-based NOx emission factor exhibited a bimodal distribution at 27 and 41 g/kg. The fuel-based emission factors for volatile organic compounds (VOCs) were in the range of 0.8 to 2.6 g/kg, where alkene and alkane were the dominant components (>80%), i.e., ethylene, acetylene, propylene, and isobutane. We observed that the ratio of toluene and benzene concentration (T/B) (1.4 ± 1.3) differed from other key emission sources and may be used as the specific indicator of DCV emission exhaust. Our estimates suggest that in 2017 the fuel consumption and NOx emissions of DCV emission accounted for 22-28% of non-road mobile sources in China; NO_X emissions were 2.7 times higher than those in 2006, and it is forecasted that NOx emissions would reduce by 23% between 2017 and 2025 with the implementation of stage IV and the strict supervision policy. The comprehensive dataset on DCV emissions will either guide the government to establish precise and effective policies to regulate the non-road mobile source or significantly improve our understanding of source apportionment of atmospheric NOx and VOCs, both of which are key precursors of haze and ozone pollution.

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.