Major Atmospheric Pollutants Research Articles

The effect of the ultra-low emission zone on PM2.5 concentration in Seoul, South Korea

As studies regarding the positive effect of Low Emission Zones (LEZs) and people's risk perception about air pollution have increased, more powerful and specific traffic regulation policies have been required. London is the first city in the world to implement an Ultra-low Emission Zone (ULEZ) in addition to the existing LEZ. Benchmarking London's ULEZ, a ULEZ policy was implemented in Seoul, South Korea on December 1, 2019. The goal of the policy is to improve air quality by prohibiting entry of vehicles registered nationwide into Seoul's ULEZ that do not meet a specific emission standard including diesel, gasoline, and LPG fuel-based vehicles. This study analyzed the effect of Seoul's ULEZ policy on the five major atmospheric pollutants (PM2.5, PM10, NO2, CO, SO2, O3) concentration in the zone, particularly focusing on PM2.5 concentration. The analysis employs Difference-in-Differences (DD) approach, comparing data from one year before and after the policy's implementation on December 1, 2019. The findings indicate that Seoul's ULEZ policy resulted in a 9.8% increase in PM2.5 concentrations. Conversely, the policy led to reductions in PM10, NO2, CO, and SO2 concentrations by 12.0%, 17.3%, 5.9%, and 10.8%, respectively, while the effect on O3 was statistically insignificant. These empirical results suggest that the ULEZ may need to incorporate more stringent emission standards, expand its coverage, or introduce additional measures to address the unintended increase in PM2.5 concentration.

A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods

PM2.5 is still one of the major atmospheric pollutants worldwide. Extracting contributions of anthropogenic emission control from the observed PM2.5 variations (PM2.5_anth), which are also strongly affected by meteorological changes, is critical for effective pollution control. Statistical and machine learning methods are usually used for such purpose, but the effectiveness of these methods is hard to evaluate due to the lack of observed anthropogenic contributions. In this study, we use the chemical transport model GEOS-Chem standard simulation to mimic PM2.5 variability in the real atmosphere, and use the model simulation with fixed meteorological fields as the “true value” for PM2.5_anth. We evaluate the effectiveness of three methods in meteorological normalization of PM2.5 on decadal (2006–2017) and synoptic (one month) scale: multiple linear regression (MLR), general additive model (GAM), and random forest (RF) algorithm. For meteorological normalization of PM2.5 on decadal scale, 67–72% of the MLR simulations show positive biases and 56–75% of the RF simulations show negative biases. The “true value” of PM2.5_anth falls within the range of meteorological normalization results of the three methods in most cases, but consistent positive/negative biases are observed in ∼30% of the cases, when meteorological changes dominate PM2.5 variability. In addition, the biases are correlated to the contribution of meteorological changes. As such, multiple statistical or machine learning methods are recommended to quantify the uncertainties associated with method choice in cases anthropogenic emission changes dominate PM2.5 variability. On synoptic scale, RF performs better in reproducing the daily variations of the PM2.5_anth differences than MLR (GAM) in all (83% of) the cases, and is recommended for meteorological normalization of PM2.5 in short-term in eastern China.

Recent Trends in Air Pollution in the Most Important City of The Romanian Black Sea Coast

Air pollution is one of the greatest environmental issues of contemporary society, affecting virtually all major cities worldwide, including those in Romania. Consequently, evaluating the dynamics of pollution conditions in urban environments is essential for designing control measures and adaptation strategies to this environmental disruption. This paper aims to analyze the recent dynamics of pollution and air quality in Constanta, the largest city on the Romanian coastline and one of the most important cities along the entire Black Sea coast. The investigations were based on the official concentrations of major atmospheric pollutants recorded at air quality monitoring stations located within or near Constanta. The dynamics of pollutant concentrations were explored at annual and seasonal levels, and the results showed a mixed picture of changes in the atmospheric pollutant concentrations, in recent years. Essentially, the findings highlighted both increases and decreases in air pollution conditions, emphasizing the need for constant air quality monitoring and controlling throughout Constanta city.

Mapping air quality variations in industrial and agricultural zones: Understanding spatial and temporal patterns of selected air pollutants

With the escalating threat of air pollution on the biota, the need for research on pollutants has become imperative in industrial, urbanized and heavily populated cities. This study aimed to determine the level of major outdoor atmospheric pollutants (PM10, PM2.5, TVOCs, and HCHO) in specific locations including industrial and agricultural zones. Field measurements for this research, spanning 52 locations, were collected in the spring season using a portable instrument, and spatial variation maps were developed using ArcGIS software. The study measured PM10 concentrations ranging from 31 μg/m3 to 267 μg/m3 and PM2.5 concentrations ranging from 27 μg/m3 to 253 μg/m3. PM2.5 levels at most sites exceeded USEPA, WHO, and PEQS standards, while PM10 levels generally met these standards. The highest values of coarse and fine particulate matter were observed in areas dominated by emissions from the textile industry. Furthermore, particulate matter concentrations showed spatial variations, increasing towards the North and East directions. The Total Volatile Organic Compounds (TVOCs) concentrations, ranging from 0 mg/m3 to 4.019 mg/m3, exceeded Hong Kong SAR standards at five locations, thereby elevating health risks for residents in these areas. Similarly, formaldehyde levels, ranging from 0 mg/m3 to 0.754 mg/m3, surpassed the Hong Kong SAR standard at seven locations, indicating a substantial threat of exposure for the local population. In general, air quality at most field sampling sites was registered as UNQUALIFIED but was also observed to be FRESH at a few locations. This research is significant for understanding the levels of major pollutants so that the concerned authorities can take appropriate measures to tackle air pollution by introducing pollution control technologies in industries, switching to renewable energy options, and managing road dust.

Characteristics and Source Apportionment of Volatile Organic Compounds in a Coastal Industrial Area: A Case Study in the Yangtze River Delta of China.

In recent years, the coastal area in East China has experienced elevated volatile organic compounds (VOCs) levels during specific periods. VOCs have become one of the major atmospheric pollutants in these areas. In this study, 64 compounds including alkanes, alkenes, halohydrocarbons, aromatics, and oxygenated VOCs (OVOCs) were obtained by the TO-15 method through a 12-month campaign in industrial, urban and suburban areas in the Yangtze River Delta of China. The overall trends of total VOC (TVOC) concentrations at eight sampling sites were as follows: winter > autumn > spring > summer. The proportion of VOC categories was various at industrial sites, while OVOCs and halohydrocarbons had high proportions at urban sites and suburban sites, respectively. Coating, vehicle emission, petrochemical source, industrial source, and gasoline volatilization were identified as the major VOC emission sources by the positive matrix factorization model. Petrochemical and coating sources were the prime VOC sources at industrial sites. Aromatics contributed the most ozone formation potential at industrial sites, while OVOCs provided the main contributions at both urban and suburban sites during four seasons. According to the health risk assessment, a high probability of non-carcinogenic risk existed at three industrial sites. Special attention should be given to certain VOCs, such as acrolein and 1,2-dibromoethane in industrial areas.

Analysis of atmospheric pollutant characteristics and regional transport in coastal area along the East China Sea

PM2.5 and black carbon (BC) are important air pollutants impacting radiation balance, air quality, health, and ecosystems. Ozone (O3) levels are increasing despite decreases in other pollutants, posing a challenge for pollution control, especially in coastal cities like Zhoushan, where the monsoonal climate can exacerbate PM2.5 and ozone pollution. This study conducted continuous online measurements of major atmospheric pollutants in Zhoushan, Zhejiang Province, in 2020. The results indicate that the highest contribution from local air masses in Zhoushan is observed in spring, accounting for 17.7 %, while the greatest average contribution from northern Zhejiang Province, Jiangsu Province, and Shanghai occurs in winter, at 18.5 %. Pollutant concentrations were seasonally variable, with PM2.5, BC, and sulfur dioxide concentrations 56.6 %, 36 %, and 58.2 % higher in the cold season compared to the warm season. The O3 in spring is approximately 50 % higher than that in summer. Ship emissions significantly contributed to BC, nitrogen oxides (NOx), and carbon monoxide in Zhoushan. In spring, PM2.5 sources included photochemical processes and northern air mass transport, while in winter, PM2.5 was due to regional transport. The inhibitory effect of PM2.5 on O3 formation in the Zhoushan area is relatively weak. Reducing NOx emissions may increase O3, emphasizing the need for volatile organic compounds monitoring and regional control measures to improve air quality and ensure sustainable development in Zhoushan.

Mitigating air pollution benefits multiple sustainable development goals in China

Achieving the United nations 2030 Sustainable Development Goals (SDGs) remains a significant challenge, necessitating urgent and prioritized strategies. Among the various challenges, air pollution continues to pose one of the most substantial threats to the SDGs due to its widespread adverse effects on human health and ecosystems. However, the connections between air pollution and the SDGs have often been overlooked. This study reveals that out of the 169 SDG targets, 71 are adversely impacted by air pollution, while only 6 show potential positive effects. In China, two major atmospheric nitrogen pollutants, ammonia and nitrogen oxides, resulted in an economic loss of 400 billion United States Dollar (USD) in 2020, which could be reduced by 33% and 34% by 2030, respectively. It would enhance the progress towards SDGs in China by 14%, directly contributing to the achievement of SDGs 1 to 6 and 11 to 15. This improvement is estimated to yield overall benefits totaling 119 billion USD, exceeded the total implementation cost of 82 billion USD with ammonia as the preferential mitigation target. This study underscores the importance of robust scientific evidence in integrated policies aimed at aligning improvements in environmental quality with the priorities of sustainable development.

Regulation of open straw burning and residential coal burning around urbanized areas could achieve urban air quality standards in the cold region of northeastern China

Air quality issues in Northeast China are concerning due to the significant seasonal atmospheric pollution and typical climatic, geographic and industrial structure characteristics of the area. However, urban air quality managers are having great difficulty achieving targeted pollution control goals in the time allotted due to limited studies in the region. In this study, using the city of Siping in Jilin Province as a case study, we analysed the cause of seasonal atmospheric pollution, developed a high-resolution emissions inventory for major atmospheric pollutants and simulated urban air quality improvements under projected emission control scenarios to select a reasonable source control scheme to inform urban air quality management planning in the region. Years with available air quality data (2014–2020) were divided into three periods, i.e., a weak control period (WCP: 2014–2017), an enhanced control period (ECP: 2018–2019) and the COVID-19 period (CDP: 2020). The annual PM2.5, PM10, SO2 and NOx emissions varied by 62∼46 μg·m−3, 103∼76 μg·m−3, 27∼22 μg·m−3 and 46∼41 μg·m−3, respectively, during the WCP; the emissions decreased to 37 μg·m−3, 69 μg·m−3, 11 μg·m−3 and 34 μg·m−3, respectively, during the ECP and to 33 μg·m−3, 61 μg·m−3, 11 μg·m−3 and 33 μg·m−3, respectively, during the CDP, which reached the limits of the Ambient Air Quality Standard of China (level II, 35 μg·m−3, 70 μg·m−3, 60 μg·m−3 and 50 μg·m−3). The PM2.5 and PM10 indices exceeded the standards, mainly in late autumn (October and November) and in the middle of winter (January and February). Based on comprehensive data analysis of the spatiotemporal air quality and the local high-resolution emissions inventory, straw burning, stationary combustion (especially for coal burning) and vehicle emissions were the main pollution sources in late autumn, and continuous adverse weather conditions were the dominant factors in severe pollution days during the middle of winter. In addition, the urban air quality of Siping was also affected by pollutant transport from the northwest (the city of Songyuan in Jilin Province), northeast (the city of Changchun in Jilin Province) and south (the city of Tieling in Liaoning Province) according the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Strict straw-burning policy was implemented in autumn during the ECP and CDP, which significantly improved the air quality from that of the WCP, with an AQI reduction of 31.58 %. However, the planned burning policy was not well-implemented, resulting in more days of moderate or heavy pollution. Using 2017 as the base year, scenarios were simulated in which feasible source control measures were set (namely, straw-burning control, the elimination of small boilers in urbanized areas, staggered peak heating measures and a 50 % reduction in residential bulk coal burning around urbanized areas and suburban areas), and the concentrations of PM2.5 and PM10 were projected to decline by more than 29.79 %, meeting the urban air quality standards. This study highlights the importance of combining straw burning regulation in no-burning areas, limited burning areas and residential coal-burning substitution around urbanized areas to improving the air quality in cold regions.

Experimental study on three additives used for the removal of nitrite, a byproduct of ozone oxidation denitration technology

Nitrogen oxides resulting from fossil fuel combustion are one of the major atmospheric pollutants. Ozone oxidation flue gas denitration technology has garnered significant attention for its benefits such as high efficiency, low temperature and simultaneous removal of multiple pollutants. However, the large amount of nitrite in the absorption products makes it prone to secondary pollution. A cost-effective and harmless treatment of the by-product nitrite will promote the ozone oxidation denitration technology. This study focused on investigating three different additives (ammonium chloride, ammonium carbonate, and urea) that can be added to react with nitrite and remove it in a harmless way. The effects of reaction temperature, initial nitrite ion concentration, reaction molar ratio, and initial pH on nitrite removal efficiency were explored. Combined with a wet spraying system, the synergistic removal of NOx/SO2 and by-product nitrite has also been studied. The experimental results revealed that with appropriate operating conditions and additives, the by-product nitrite can be efficiently removed while the system can achieve efficient removal of NOx/SO2. Among the three additives, ammonium carbonate was found to have the best overall removal effect. However, urea had an advantage over the other two additives in highly acidic and low-temperature slurry.

Spatial-temporal heterogeneity and driving factors of PM2.5 in China: A natural and socioeconomic perspective.

Fine particulate matter (PM2.5), one of the major atmospheric pollutants, has a significant impact on human health. However, the determinant power of natural and socioeconomic factors on the spatial-temporal variation of PM2.5 pollution is controversial in China. In this study, we explored spatial-temporal characteristics and driving factors of PM2.5 through 252 prefecture-level cities in China from 2015 to 2019, based on the spatial autocorrelation and geographically and temporally weighted regression model (GTWR). PM2.5 concentrations showed a significant downward trend, with a decline rate of 3.58 μg m-3 a-1, and a 26.49% decrease in 2019 compared to 2015, Eastern and Central China were the two regions with the highest PM2.5 concentrations. The driving force of socioeconomic factors on PM2.5 concentrations was slightly higher than that of natural factors. Population density had a positive significant driving effect on PM2.5 concentrations, and precipitation was the negative main driving factor. The two main driving factors (population density and precipitation) showed that the driving capability in northern region was stronger than that in southern China. North China and Central China were the regions of largest decline, and the reason for the PM2.5 decline might be the transition from a high environmental pollution-based industrial economy to a resource-clean high-tech economy since the implementation the Air Pollution Prevention and Control Action Plan in 2013. We need to fully consider the coordinated development of population size and local environmental carrying capacity in terms of control of PM2.5 concentrations in the future. This research is helpful for policy-makers to understand the distribution characteristics of PM2.5 emission and put forward effective policy to alleviate haze pollution.

Improvement and Uncertainties of Global Simulation of Sulfate Concentration and Radiative Forcing in CESM2

AbstractSulfate is a major atmospheric pollutant and radiative forcing (RF) factor that influences air quality, cloud microphysics, and climate. Therefore, a better evaluation of sulfate concentrations and RF patterns is essential for policy‐making and the management of air pollution and climate change. This study comprehensively estimates the global distribution of sulfate concentrations and RFs and analyzes the sources of uncertainty in the Community Earth System Model version 2 and the Parallel Offline Radiative Transfer model. Compared with the observations, the incorporation of detailed in‐cloud aqueous‐phase chemistry and the enhanced wet deposition (WD) flux of sulfate significantly improved the simulations of sulfur species both near the ground and at high altitudes, which is beneficial for a more accurate estimation of the global RF of sulfate. The improved simulated RF of sulfate from 1850 to 2015 is −0.502 Wˑm−2. This study finds that WD is the key process governing both the horizontal and vertical distributions of sulfate concentrations. The overestimation of surface sulfate and the underestimation of high‐altitude sulfate made by the model are essential uncertainty factors of the sulfate RF estimation. This study emphasizes the importance of improving the simulation of global sulfate distribution as well as its RF, which may strongly pressure the near‐future warming potential when witnessing a rapid transition to a carbon neutral world that is phasing out fossil fuel. A more accurate assessment of sulfate levels and radiation effects will play a remarkable guiding role in the formulation of global emission reduction‐related policies in the future.

Impact of tree species diversity on throughfall deposition in a young temperate forest plantation

Throughfall deposition is an important pathway via which particles, aerosols and gases can move from the atmosphere to the forest floor, which can greatly impact forest biodiversity and functioning. Although throughfall deposition biochemistry has been well studied in forest ecosystems, less is known about how throughfall deposition is modified by tree species diversity. To disentangle the effects of tree species identity and diversity on throughfall deposition, we installed rain gauges in a 10-year-old tree diversity experiment. With these rain gauges, we monitored throughfall biweekly, and performed chemical analyses monthly for all the major ions (Na+, Cl−, SO42−-S, K+, Ca2+, Mg2+, PO43−-P, NO3−-N and NH4+-N), for a period of one year. Based on these data, we analysed species identity (i.e. whether throughfall deposition depended on the tree species present in the overstorey), and diversity effects (i.e. whether throughfall deposition depended on tree species richness). We confirmed species identity effects on throughfall deposition. Presence of pine led to higher throughfall deposition of inorganic nitrogen, and lower amounts of phosphorus, calcium and magnesium reaching the forest floor. Additionally, species characteristics and stand structural characteristics, i.e. basal area and canopy cover, emerged as key factors driving throughfall deposition of sodium, chloride, nitrate, sulphate and potassium. Tree species diversity effects on throughfall deposition were also present, mostly via increased basal area and canopy cover, leading to increased throughfall deposition of inorganic nitrogen, sulphate, chloride, sodium and potassium. Our findings thus suggest that tree species diversity enhances throughfall deposition of most ions that we analysed, potentially increasing nutrient availability for below-canopy plant communities. Given that inorganic nitrogen and sulphur are major atmospheric pollutants, increased deposition in mixed plots might affect ecosystem functioning. Further research will be needed to determine the extent of this impact.

Restricted Anthropogenic Activities and Improved Urban Air Quality in China: Evidence from Real-Time and Remotely Sensed Datasets Using Air Quality Zonal Modeling

The study aims to examine the major atmospheric air pollutants such as NO2, CO, O3, PM2.5, PM10, and SO2 to assess the overall air quality using air quality zonal modeling of 15 major cities of China before and after the COVID-19 pandemic period. The spatio-temporal changes in NO2 and other atmospheric pollutants exhibited enormous reduction due to the imposition of a nationwide lockdown. The present study used a 10-day as well as 60-day tropospheric column time-average map of NO2 with spatial resolution 0.25 × 0.25° obtained from the Global Modeling and Assimilation Office, NASA. The air quality zonal model was employed to assess the total NO2 load and its change during the pandemic period for each specific region. Ground surface monitoring data for CO, NO2, O3, PM10, PM2.5, and SO2 including Air Quality Index (AQI) were collected from the Ministry of Environmental Protection of China (MEPC). The results from both datasets demonstrated that NO2 has drastically dropped in all the major cities across China. The concentration of CO, PM10, PM2.5, and SO2 demonstrated a decreasing trend whereas the concentration of O3 increased substantially in all cities after the lockdown effect as observed from real-time monitoring data. Because of the complete shutdown of all industrial activities and vehicular movements, the atmosphere experienced a lower concentration of major pollutants that improves the overall air quality. The regulation of anthropogenic activities due to the COVID-19 pandemic has not only contained the spread of the virus but also facilitated the improvement of the overall air quality. Guangzhou (43%), Harbin (42%), Jinan (33%), and Chengdu (32%) have experienced maximum air quality improving rates, whereas Anshan (7%), Lanzhou (17%), and Xian (25%) exhibited less improved AQI among 15 cities of China during the study period. The government needs to establish an environmental policy framework involving central, provincial, and local governments with stringent laws for environmental protection.

Mechanism of ozone-assisted catalytic oxidation of isopropanol over single-atom platinum catalysts at ambient temperature

Volatile organic compounds (VOCs) have become the major atmospheric pollutants in many countries. To develop more efficient VOCs catalytic oxidation technologies, in-depth study of degradation mechanism of VOCs is necessary and urgent. Although the catalytic degradation of small-molecule VOCs such as methanol and formaldehyde is relatively well known, the degradation mechanism of multi-carbon VOCs is largely unclear. Here we construct a single atom Pt catalyst supported on Na-doped TiO2 (Na-Pt/TiO2) for degrading isopropanol into CO2 and H2O at a temperature as low as 35 °C. We further reveal the promoting effect of Na in the catalyst, the formation process of hydroxyl radicals and, the catalytic degradation mechanism of isopropanol through combination of sophisticated experimental studies and density functional theory (DFT). This work not only devises a new catalyst for the degradation of isopropanol at ambient temperature, but also provides a new insight into the mechanism of catalytic oxidation of multi-carbon VOCs.

Spatio-Temporal Monitoring of Atmospheric Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of Stubble Burning a Case Study

The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.

Plant Ontogeny Strongly Influences SO2 Stress Resistance in Landscape Tree Species Leaf Functional Traits

Sulfur dioxide (SO2) is a major atmospheric pollutant and abiotic stressor. Although physiological studies on abiotic stressors have focused on fully expanded leaves, the resistance of leaf functional traits to SO2 during individual leaf development has not been studied. Thus, this study aimed to conduct SO2 static artificial fumigation experiments to evaluate changes in leaf functional traits and resistance to SO2 for three common landscape tree species (Syringa oblata Lindl. (S. oblata), Prunus cerasifera var. atropurpurea Jack. (P. cerasifera), and Ulmus pumila ‘Jinye’ (U. pumila)) in Changchun City and ontogeny under SO2 stress. Samples were collected on three days in autumn (1 September, 9 September, and 19 September 2019) for two different leaf stages (10 days and 40 days). In addition, remote sensing data were combined to explore the resistance mechanisms of broadleaf forests to different SO2 concentration classes during different seasons on a large scale. The results showed that the chlorophyll content, leaf temperature, green-peak reflectance, and Fv/Fm (maximal photochemical efficiency) at 10 days were significantly lower than that at 40 days, regardless of sampling date or SO2 concentration. Additionally, in general the SO2 resistance for 10 days leaves was consistently smaller than those for 40 days leaves in 3 tree species. On 9 September, 10 days leaves of the three tree species showed different leaf resistance performances under different SO2 concentrations in the order: P. cerasifera > S. oblata > U. pumila. Lastly, the extent of resistance decreased with increasing ρ(SO2) classes in different seasons, and the SO2 resistance was affected by season. We conclude that mature leaves are more resistant to SO2 stress than young leaves are. These results will provide scientific guidance on artificial plant community construction and prevention of future vegetation degradation.

Remote sensing of vehicle emitted carbon oxides employing wavelength modulation spectroscopy and a segment modulation method

AbstractVehicle exhaust emission is one of the major atmospheric pollution sources. Excessive emitters, which make up 10 percent of all vehicles, contribute more than half of the total pollution emission. To identify these excessive emitters, a remote‐sensing device (RSD) with a laser wavelength around 1580 nm is built. To increase the system sensitivity, a segment modulation method is proposed so that the 2f signals of CO2 and CO can be optimized, respectively. In vitro tests show that the linearity reached .999 and .998 for CO2 and CO, respectively. A field test is also conducted to monitor the dilution process of exhaust emission after the vehicle passes the RSD.

Properties of Microconcrete Produced with Fly Ash and Ultrafine Silica

Abstract Microconcrete is an important subclass of regular concrete that lacks the coarse aggregate commonly found in standard concrete. This makes it a good candidate for certain casting operations, such as densely reinforced elements. Microconcrete also tends to have better workability and smoother flow in formwork elements than regular concrete. However, one of humanity’s current problems is air pollution caused by chemical, physical, or biological agents with harmful effects on the environment. Carbon dioxide (CO2) resulting from cement production is a major atmospheric pollutant. To align with sustainable development standards, the construction industry must seek alternative materials that are more environmentally friendly. This article presents a comparison of the properties of regular microconcrete and microconcrete with additives such as fly ash and ultrafine silica. This topic is important because environmental issues are becoming more pressing. The results show that using waste products as additives in microconcrete can provide a sustainable solution for reducing carbon dioxide emissions and environmental impact. Although lower strengths were obtained compared to traditional concrete, microconcrete with waste products can be used in various applications, such as the production of prefabricated construction elements or the rehabilitation of existing structures. Additionally, it can be used in the construction of paving stones, sidewalks, or industrial floors, contributing to reducing pollution and environmental impact.

High-resolution mapping of regional traffic emissions using land-use machine learning models

Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing–Tianjin–Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.

Estimation and Spatiotemporal Analysis of NO2 Pollution in East Asia During 2001–2016

AbstractAmbient nitrogen dioxide (NO2) not only has adverse health effects on humans but also contributes to the production of two major secondary atmospheric pollutants, ozone (O3) and fine particulate matter (PM2.5). In this study, surface NO2 concentrations in East Asia from 2001 to 2016 were estimated by combining an ensemble backpropagation neural network method, satellite NO2 column data, and reanalysis data. The estimated monthly and annual mean NO2 concentrations were well‐correlated with the observations, with R (correlation coefficient) values of 0.89 and 0.91, respectively. Our results indicate that the NO2 concentrations in most areas of East Asia peaked during 2011–2013. The NO2 concentrations in autumn and winter, especially in the eastern and northern parts of China, were much higher than those in summer. In terms of population NO2 exposure, over 25 million South Korea residents (∼45% of the population) were exposed to NO2 concentrations higher than the 2005 World Health Organization's annual standard (40 μg/m3, ∼22 ppbv at 25°C) in 2015. In contrast, the entire populations of some developing countries, such as Vietnam, Cambodia, Myanmar, the Philippines, and Lao PDR, were exposed to NO2 concentrations less than 14 ppbv. Based on the estimation, NO2‐related asthma cases in East Asia increased by 1.37% annually from 2001 to 2015, reaching 139,000 cases (95% confidence interval: 37,400–263,400) in 2015. NOx emission inventories vary from country to country in East Asia; thus, more targeted NOx emission‐control policies are urgently required.