Local Pollution Research Articles

Effects of the Emissions of Vehicles Ahead on In‐Car Exposure to Traffic‐Related Air Pollutants: A Multiple Statistical Analysis Approach

Traffic‐related air pollutants inside vehicle cabins are often extremely high compared to background pollution concentrations. The study of the determinants of these concentrations is particularly important for professional drivers and commuters who spend long periods in vehicles. This study is aimed at identifying and quantifying the effect of several exposure determinants on carbon monoxide (CO), equivalent black carbon (eBC), two particulate matter (PM) fractions (PM0.3–1 and PM1–2.5), and ultrafine particle (UFP) concentrations inside a passenger car cabin. The novelty of this work consists in examining the effects of the emissions of the first vehicle ahead (henceforth called “leading vehicle”) on pollutant concentrations inside the cabin of the following vehicle (i.e., the car that was equipped with the air monitoring devices), with particular emphasis on the role of the leading vehicle characteristics (e.g., emission reduction technologies). The real‐time instrumentation was placed inside the cabin of a petrol passenger car, which was driven by the same operator two times per day on the same route in real driving conditions. The in‐cabin ventilation settings were set as follows: windows closed, air conditioning and recirculation modes off, and the fanned ventilation system on. The measurements were conducted over a total of 10 weekdays during two different seasons (i.e., summer and autumn). A video camera fixed to the windscreen was used to retrieve information about traffic conditions and leading vehicle characteristics through careful video analysis. The associations among pollutant concentrations and their potential determinants were evaluated using generalized estimating equation univariate and multiple models. The results confirmed the significant impact of several well‐known determinants such as seasonality, microclimatic parameters, traffic jam situations, and route characteristics. Moreover, the outcomes shed light on the key role of leading vehicle emissions as determinant factors of the pollutant concentrations inside car cabins. Indeed, in the tested cabin ventilation conditions, it was demonstrated that in‐cabin pollutant concentrations were significantly higher with leading vehicles ahead (from +14.6% to +67.5%) compared to empty road conditions, even though the introduction of newer technologies with better emissions reduction helped mitigate their effect. Additionally, diesel‐fuelled leading vehicles compared to petrol‐fuelled leading vehicles were impactful on in‐cabin CO (−7.2%) and eBC (+45.3%) concentrations. An important effect (+30.4%) on in‐vehicle PM1–2.5 concentrations was found with heavy‐duty compared to light‐duty leading vehicles. Finally, this research pointed out that road‐scale factors are more important determinant factors of in‐cabin concentrations than local pollution and meteorological conditions.

How to perceive and map the synergy between CO2 and air pollutants: Observation, measurement, and validation from a case study of China

Cities occupy a central position in addressing climate change and promoting sustainable regional development. Synergistic control of urban gas emissions at the city level is one of the main issues typically explored. The confounding effect and the interactions between the urban indicators of population and area have been ignored in previous studies. In this study, we examined the spatial distribution characteristics and synergy between greenhouse gases (CO2) and air pollutants (SO2 and NOX) using spatial population and gas emission data. By upgrading the city clustering algorithm (CCA), we established a method for defining active areas of gas emissions (spatial element-coupled clustering, SECC) and identified active areas of gas emissions in China. In this study, we created a research framework that can simultaneously consider the effects of population and area, as well as the possible interactions between these indicators in active areas. The superlinear scaling relationship between the above three gases was revealed at the active zone level, and the existence of synergy between the emission patterns of the three gases was confirmed. Via further model application, we measured the synergistic efficiency of the three gases. It was found that for every 1% increase in SO2 and NOX in an active zone, CO2 increases by 0.86%. In this study, we explored a new perspective and approach to explain the synergy between greenhouse gases and air pollutants. This is essential to promote national competition among cities to achieve synergistic control of CO2 and local air pollutants.

Spatial patterns of PM2.5 air pollution in Jakarta: Insights from mobile monitoring

A mobile air quality monitoring study in Jakarta was undertaken in 2022 by the Clean Air Catalyst consortium, in partnership with Google Earth Outreach and Aclima, taking air pollution measurements using four specially equipped Google Street View cars. This study aims to map street-level PM2.5 pollution in a set of neighborhoods (polygons) throughout Jakarta to evaluate the variability caused by local emission sources and to observe the exposure to air pollution in areas with different income levels. To address these aims, we monitored the concentration five days a week, 8 am to 5 pm, from September to December in eight polygons, stratified to include high and low-income areas. The cars covered a distance of 7,750 km and at least 22 visits per location. PM2.5 concentrations were measured at 1-second intervals using a light-scattering sensor. This high-resolution data collection allowed for detailed monitoring and analysis of concentrations in the study area. The measurements showed temporal variability consistent with meteorological and activity patterns in Jakarta, with higher measured concentrations in the morning compared to the afternoon at most polygons. Median concentrations varied by 2x - >5x within individual polygons, and this variability was particularly evident in the northern part of Jakarta, encompassing both high-income and low-income neighborhoods, where these areas are significantly affected by traffic and industrial activities. The results demonstrate the extent to which PM2.5 concentrations and exposures within Jakarta neighborhoods depend on proximity to local air pollution sources.

Assessment of particulate matter (PM) in ambient air of different settings and its associated health risk in Haripur city, Pakistan.

Particulate matter (PM) is a major air pollutant causing serious health problems. The aim of the present study was to find out concentration of PM in ambient air and its associated health risk in Haripur city, Pakistan. Twenty-three samples were taken at various educational institutes, hospitals, recreational areas and industries in Haripur city. Concentration of PM2.5 (µg/m3) and PM10 (µg/m3) was measured with Youngteng YT-HPC 3000A portable PM counter. The results revealed that values of both PM2.5 and PM10 were above the permissible limits (35 µg/m3 for PM2.5 and 150 µg/m3 for PM10) set by Environmental Protection Agency Pakistan (Pak-EPA) in all the educational institutes, hospitals, recreational areas and industries investigated. Furthermore, significant (p<0.05) variation was found in the concentration of both PM2.5 and PM10 in all the educational institutes, hospitals, recreational areas, and industries studied. The concentration of PM2.5 was positively correlated with the concentration of PM10 in all the sampling sites. Therefore, from 1-14 scale standard of health index, the values of PM2.5 and PM10 exhibited that the ambient air quality of Haripur city Pakistan is under high risk. If the regulatory authorities such as Environmental Protection Agency, Health Department and Local Government monitor PM pollution in different settings of Haripur city, then a decrease can be possible in the pollution level. The remedies that can be taken to overcome the problem of ambient air pollution such as PM are plantation of trees at the sites where there are higher levels of air pollutants and use of masks on personal protection basis along with implementation of pollution control system in industries of Hattar Industrial Estate Haripur city, Pakistan.

Industrieschnee: An overview of some European anthropogenic snowfall events, with a case study from Bern (Switzerland)

AbstractIndustrieschnee or anthropogenic snowfall is a local winter hazard in many European cities and towns under suitable anticyclonic conditions and in the vicinity of persistent local water vapour sources and air pollution. Here, we overview the formation of Industrieschnee and describe a case study event from Bern, Switzerland, comparing the estimated mass of snow deposited with the water vapour emissions from a local incinerator. We speculate that previous European Industrieschnee events may have been considerably under‐reported, or falsely attributed to surface hoar frost (reif) or the accretion of rime (raureif).

Estimation of local pollutant and carbon dioxide emissions of road and rail transportation in the Philippines

The 2016 baseline road and rail transportation pollutant emissions in the Philippines using a bottom-up approach are estimated at 9.212 million tons and 377 tons, respectively, while CO2 emissions are estimated at 97.6 million tons and 62,248 tons, respectively. Road transportation activity is estimated to be 231.53 billion vehicle-kilometers, where 58% is from motorcycles. Top pollutants are CO and VOC from motorcycles, tricycles and cars. Motorcycles contribute the largest share of CO2 emissions. NOₓ and CO are major pollutants from rail transportation. Based on these results and the assessment of selected transport projects, the government may develop policies on major emitters of criteria pollutants and carbon dioxide, aside from improvement of public transportation. Furthermore, the study is limited in using average vehicle emission factors which need updating.

Sick building syndrome: do outdoor pollutants and pollen affect it?

Sick building syndrome (SBS) refers to non-specific complaints, including upper-respiratory irritative symptoms, headaches, fatigue, and rash, which are usually associated with a particular building by their temporal pattern of occurrence and clustering among inhabitants or colleagues. The aim of the study was to determine the association between the clinical manifestations of sick building syndrome with outdoor pollutants and airborne pollen. It was a descriptive and prospective observational study conducted from November 2021 to April 2022. It included subjects over 18 years old who completed an online survey on sick building syndrome (general symptoms, nasal, ocular, oropharyngeal, and skin symptoms) presented at home, housing information and personal history. The APS-330 from Pollen Sense ® was used to obtain data on pollen in the air and the local pollution monitoring system (SIMA) to obtain information regarding pollutants. For statistical analysis, SPSS version 16 was used. A total of 402 surveys were included; 91% of the subjects reported having at least 1 symptom. Females presented more general symptoms (fatigue and headache) than males. Subjects with a personal history of atopy showed a higher prevalence of practically all symptoms. Airborne pollen exposure was positively associated with mucosal symptoms in eyes and nose. Outdoor fungi spore exposure was positively associated with oculo-nasal and cutaneous symptoms in the scalp. This study found significant associations with female gender and a history of atopy, which suggests a higher risk for these subjects. Despite the limitations of the study, we can conclude that there is an association between the clinical manifestations of sick building syndrome with indoor and outdoor pollution.

Spatiotemporal Variation of Groundwater Nitrate Concentration Controlled by Groundwater Flow in a Large Basin: Evidence From Multi‐Isotopes (15N, 11B, 18O, and 2H)

AbstractElevated and increasing concentration in groundwater affect groundwater supplies in China and elsewhere. However, how groundwater flow affects concentration in groundwater has yet to be fully understood. Herein, multi‐isotopes (15N, 11B, 18O, and 2H) and local indicators of spatial association (LISA) were used to elucidate the spatiotemporal variation, sources, and patterns of and its response to groundwater flow in Poyang Lake Basin where agriculture, industry and urban coexist. The location of hotspots identified by LISA tended to move from the middle to lower reaches of Ganfu Plain with groundwater flow, and hotspots area expanded in the upper reaches of Xin River Basin and northwest of the study area during the transition from dry season to wet season. Our results revealed that variations of regional concentration were controlled by groundwater recharge or flow mode (vertical or lateral), biogeochemical processes and sources (sewage and manure). In some areas with the single stratigraphic structure (unconfined aquifer), spatiotemporal variation of concentration was influenced by local pollution sources and vertical recharge of current precipitation (vertical flow). In some areas with binary structures (confined aquifer), groundwater was mainly recharged by lateral flow and concentration was mainly affected by mixing effect of upstream groundwater, reflecting human activities in the upper reaches rather than local human activities. In lakeside floodplain, groundwater was attenuated by the dissimilatory reduction to . This study provides a novel insight into groundwater flow controlling on spatiotemporal distribution of concentration in the regional scale.

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 μg/m3 to 44.8416 μg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Exhaust Emission Assessment of Haulage Trucks at Onne Port, Nigeria

Haulage trucks are among the primary causes of air pollution in urban areas. The effect of air pollution caused by transportation, especially freight transportation, on public health is a major concern globally. In this study, emissions of haulage trucks operating at Onne Port, Rivers State in Nigeria were assessed to understand their contribution to local air pollution and environmental impact. Real-time measurements of air pollutants were taken using a portable Testo 350 Analyzer and a portable IGRESS Intelligent Detector from seventy (70) haulage trucks at Onne Port grouped according to production years, region of origin, and truck length, and the data obtained were compared with the World Health Organization (WHO) air quality standards. The results revealed that the trucks’ emissions exceeded WHO limits for six criteria pollutants, including NO2, SO2, CO, PM1, PM2.5, and PM10, which are associated with adverse health effects such as respiratory and cardiovascular diseases. Other notable findings included higher concentrations of NO2 at 35.48 ppm and PM2.5 at 300.73 µg/m3 from trucks produced between 2011 and 2021, while older trucks exhibited increased emissions of SO2 at 31.16 ppm, CO at 775.87 ppm, PM1 at 216.75 µg/m3, and PM10 at 341.75 µg/m3. There were no significant variations in pollutant emission concentrations observed within the four categories across different production years. Trucks manufactured in Asia consistently emitted lower pollutant concentrations compared with those from other regions, suggesting the influence of varying emission standards. Interestingly, truck length did not significantly impact emission levels. These findings underscore the urgency of addressing the air quality issues associated with haulage trucks in the region, highlighting the need for stringent emissions control measures.

A comprehensive exploration of characteristics and source attribution of carbonaceous aerosols in PM2.5 in an East China megacity

A total of 84 PM2.5 (fine particulate matter) aerosol samples were collected between October 2020 and August 2021 within an urban site in Hangzhou, an East China megacity. Chemical species, such as organic carbon (OC), elemental carbon (EC), as well as char, soot, and n-alkanes, were analyzed to determine their pollution characteristics and source contributions. The mean yearly concentrations of OC, EC, char, soot, and total n-alkanes (∑n-alkane) were 8.76 ± 3.61 μg/m3, 1.44 ± 0.76 μg/m3, 1.21 ± 0.69 μg/m3, 0.3 ± 0.1 μg/m3, and 24.2 ± 10.6 ng/m3. The OC, EC, and ∑n-alkanes were found in the highest levels during winter and lowest during summer. There were strong correlations between OC and EC in both winter and spring, suggesting similar potential sources for these carbonaceous components in both seasons. There were poor correlations among the target pollutants due to summertime secondary organic carbon formation. Potential source contribution functions analysis showed that local pollution levels in winter and autumn were likely influenced by long-range transportation from the Plain of North China. Source index and positive matrix factorization models provided insights into the complex sources of n-alkanes in Hangzhou. Their major contributors were identified as terrestrial plant releases (32.7%), traffic emissions (28.8%), coal combustion (27.3%), and microbial activity (11.2%). Thus, controlling vehicular emissions and coal burning could be key measures to alleviate n-alkane concentrations in the atmosphere of Hangzhou, as well as other Chinese urban centers.

Good eutrophication status is a challenging goal for coastal waters

Our objective is to understand the effectiveness of local and international nutrient pollution mitigation efforts when targeting better water quality in the region’s coastal waters. To this end, we developed an integrated modeling framework for the Archipelago Sea located in the Baltic Sea in Northern Europe, conducted what-if analyses for various ambition levels of nutrient abatement, and studied the long-term consequences at the sea basin scale. We demonstrate that in outer parts of the Archipelago Sea, a good eutrophication status can be achieved if the current internationally agreed policy goals for nutrient abatement are successfully met. In inner coastal areas, current goals for phytoplankton biomass could be reached only through extreme mitigation efforts in all polluting sectors and large-scale application of yet poorly tested ecological engineering methods. This result calls for carefully considering the relevance of current threshold values for phytoplankton and its role as a dominant indicator of good ecological status.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics

BackgroundNational-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.ObjectiveCharacterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.MethodsWe compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed “localized difference.” We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition.ResultsWe find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations.ImpactComparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Long-Range Atmospheric Mercury Transport from Across East Asia to a Suburban Coastal Area in Southern Vietnam.

Exports of atmospheric mercury (Hg) from continental East Asia, a major Hg emitter in the globe, have been reported by several studies in neighboring countries such as Japan and Korea. Nonetheless, studies concerning this topic in Southeast Asia (SEA) countries are still limited. Accordingly, gaseous elemental mercury (GEM) has been measured from Can Thanh High School (CTHS), a suburban coastal site in southern Vietnam to study its characterization and discover the evidence of Hg trans-boundary transport from regional sources (e.g., East Asia). Data collected in July, August, and October 2022 were used in this study, and the overall GEM concentration was 1.61 ± 0.32 ng m-3. The GEM levels were higher in October than in July and August, potentially due to the discrepancy in air mass transport patterns induced by tropical monsoon and source origins of Hg. MERRA-2, backward trajectories, and CALIPSO images revealed the trans-boundary air pollution from continental East Asia to southern Vietnam, evidenced by significantly elevated (> 30%) atmospheric Hg concentrations as well as other air pollutants when the plume arrived at CTHS. Furthermore, our results also imply that atmospheric Hg exported from East Asia could influence large areas in SEA, suggesting the need for more studies in various SEA countries in the upcoming future. This study illustrated the influence of regional Hg emissions on local atmospheric Hg pollution and provided data to improve knowledge of the Hg biogeochemical cycle in SEA.

Overestimated environmental benefits of short-term changes in mobility behaviour: The case of Berlin during COVID-19

The COVID-19 pandemic changed urban mobility patterns in unprecedented ways with movement decreasing, few public transport customers, and empty streets turning into pop-up bike lanes. Accordingly, a decrease in local air pollution was reported at the start of the pandemic and models suggested a lower carbon footprint of urban mobility. Here, we offer a critical discussion of the indirect environmental impacts that can be inferred from short-lived mobility behaviour. Examining Berlin, we analyse to what extent transport volume and modal split influenced direct and indirect greenhouse gas emissions throughout the pandemic (2020–2022). The analysis combines traffic counts and survey data with process-specific life cycle assessment factors. The results show that reductions in the carbon footprint of mobility during the pandemic stem largely from a short-term decline in car use, while the modal split became more carbon intensive. Emission reductions from lower use of public transport were not realised, nor a reduction of indirect emissions from vehicle production due to slowly responding upstream demand and public transport planning. This highlights the need to revise the current assumption of perfectly responding markets in environmental footprint assessment of short-term behavioural change during crises, urban experimentation, and transitions.

Evaluation of PM2.5 spatio-temporal variability and hotspot formation using low-cost sensors across urban-rural landscape in lucknow, India

The high-resolution spatio-temporal monitoring through low-cost sensors (LCS) provides important insights into the dynamics of localized pollution patterns and in conceptualizing effective environmental management and public health interventions. We assessed the role of meteorology and local sources on spatially distributed PM2.5 pollutants measured from a unique urban-rural scaled network of LCS in Lucknow, India. The city-wide average PM2.5 annual cycle ranged between 29 ± 7 μg/m3 during the monsoon to 64 ± 30 μg/m3 during the rest of the year. During non-monsoon seasons, >60% of air mass trajectories indicated regional level transportations primarily from the north-western plains of the Indo-Gangetic basin, with peak weighted concentration weighted trajectory (WCWT, scale: 0–100) value estimated at >70. To analyze the impact of local sources, we designed a statistical classification method to divide each seasonal distribution into five incremental concentration groups ranging from no risk zone to hotspots. The hotspots were identified both within urban and rural regions with their average concentration measured as 23 μg/m3 and 26 μg/m3 higher than the rest of the regions in pre-monsoon (2 hotspots) and post-monsoon (4 hotspots) season, respectively. Further analysis shows that the nighttime concentrations were much higher in several locations (up to 40% in pre-monsoon and 54% in post-monsoon compared to their daytime levels), indicating the greater impact of local sources in the presence of low boundary layer height. A diurnal trend analysis along with conditional bivariate probability function (CBPF) was performed to interpret the characteristics and locations of the dominant sources. The study highlights the importance of a dense network of LCS to scale air quality monitoring in spatially heterogeneous environments and as a futuristic tool for PM2.5 exposure-based studies.

Fiscal policy, green finance, and low carbon transformation nexus: a novel study unleashing the synergistic effects of carbon reduction and pollution in China.

Under the simultaneous demands of combating environmental pollution and decreased carbon emissions, it is critical to investigate the combined effect of agricultural contamination reduction and fiscal policy carbon reduction to support and promote green agriculture and low-carbon transformation. Based on provincial panel data of 2007 to 2020 in China, this paper employs the spatial Dubin model to empirically examine the pollution reduction and carbon reduction effects of fiscal policies supporting agriculture, as well as calculating the synergistic effect of pollution reduction and carbon reduction. Our study's findings reveal that non-point source agricultural pollution and agricultural carbon emissions have a tendency of growing and subsequently reducing, such as increasing from 2007 to 2015 and decreasing from 2016 to 2020. Second, results demonstrate that agricultural carbon emissions and agricultural pollution have a positive geographical dependency in each province, and fiscal policies supporting agriculture have high-high and low-low spatial clustering features. Furthermore, fiscal policies that promote agriculture can lower local agricultural carbon emissions and pollution while also having a considerable beneficial spillover impact on neighboring provinces. According to the study findings, the fiscal policy for supporting agriculture has a negative pollution reduction impact and a positive synergistic effect, resulting in a synergistic effect of agricultural pollution reduction and carbon reduction. The outcomes of this study can serve to promote carbon-reduction measures and provide recommendations for future policy development.

Does executive turnover affect government transparency? Leadership changes and environmental information disclosure in Chinese cities

Research has underscored the role of executive turnover in organizational processes and outcomes. However, little research has linked turnover events to externally oriented outcomes such as government transparency, and most studies are set in Western countries. Focusing on an authoritarian context, we theorize how new executives are likely to focus on issues that align with expectations from powerful stakeholders and turnover events provide windows of opportunities for organizational changes. Using panel data from 113 Chinese cities, this article explores the impact of executive turnover on government transparency in environmental information. Results show that mayoral and party secretaries’ turnover positively affects government environmental transparency. This effect is contingent on the career stages of the successors and local pollution levels. Our results show divergence in moderating effects across mayoral turnover and party secretary turnover. Implications for research on executive turnover and government transparency in an authoritarian one-party political system are discussed.

Analysis of the particulate matter long term emissions in Romania by sectors of activities

Particulate matter (PM) in the atmosphere is highly relevant due to its far-reaching implications. These tiny solid or liquid particles affect human health, causing respiratory and cardiovascular issues, and even premature death. PM also plays a role in climate change, as some particles contribute to warming, while others have cooling effects, impacting Earth's energy balance. Additionally, PM influences visibility, transportation safety, and ecosystems by depositing on land and water surfaces, altering soil and water quality. Recognizing and monitoring the emissions and sources of PMs by 4 subclasses, from PM2.5 to PM10, but also as TSP (total suspended particles) and individually as BC – black carbon is vital for effective air quality management and mitigating its multifaceted impacts on our environment and well-being. This paper presents an analysis of PMs emissions divided into the 4 classes presented above, for Romania, between 1990-2021, as given by the national report made according to the National Emission reduction Commitments Directive (NECD), which aims to reduce emissions of the main air pollutants. The emission data are presented by national emission sectors, from different industries to different transport types, and also from agricultural activities, and waste management. Relevant averages and trends are quantified and analysed as a reference for real-time data at city level, such that to assess the local pollution level and data accuracy as compared to the national reports.

Does green credit help reduce smog pollution? Empirical evidence from China.

As a resource production factor in environmental governance activities, can green credit help reduce smog pollution? Based on China's provincial panel data from 2006 to 2019, this paper empirically tests the impact of green credit on smog pollution by using an OLS regression model and a spatial Durbin model. The results show that green credit helps to reduce smog pollution overall; Industrial structure upgrading and green technology innovation are two critical paths for green credit to reduce smog pollution. The analysis of period heterogeneity finds that the descending effect of green credit on smog pollution becomes more significant after the transformation of China's economic development stage. The regional heterogeneity analysis finds that the descending effect of green credit on smog pollution is more significant in non-low-carbon pilot provinces and regions with lower economic development levels. In addition, green credit not only helps to reduce local smog pollution but also has a spatial spillover effect of "benefiting thy neighbors" on smog pollution in geographically neighboring areas. This study provides important inspiration for the government to further promote the innovation of green financial instruments and promote the improvement of environmental governance performance, provides decision-making references for different regions to implement differentiated green credit strategies to improve ambient air quality, and provides an experience reference for the development of green finance and environmental governance in emerging market countries.