Source identification of PM2.5 at a port and an adjacent urban site in a coastal city of China: Impact of ship emissions and port activities

Analysis of aerosol chemical components and source apportionment during a long-lasting haze event in the Yangtze River Delta, China

Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta (YRD) region from 17 December 2023 to 8 January 2024, the evolutionary characteristics of the chemical components and sources of fine particulate matter (PM2.5) under different pollution levels were comparatively analyzed using PMF (Positive Matrix Factorization) and backward trajectory analysis. SNA (NO3-, NH4+, SO42-) was found to be the primary chemical component of PM2.5, making up 63.6 % (clean days) to 69.7 % (heavy pollution) of it. The NO3- concentration was 3.14 (clean days) to 6.01 (heavy pollution) times higher than that of SO42-. NO3-, POC, Fe, Mn, Al concentrations increased, while SOC, EC, crustal elements (Ca, Si) and other water-soluble ions (WSIs) concentrations decreased as the pollution level increased. The contribution of secondary inorganics and biomass-burning emissions and industrial and ship emissions increased significantly as the pollution level increased, which accounted for 40.3 % and 36.7 %, respectively, in the heavy pollution stage. The contribution of traffic sources decreases gradually with increasing pollution levels, accounting for only 59.1 % of the light pollution stage in the heavy pollution stage. PM2.5 and its main chemical components showed similar potential source distribution, located in the northwest (Fuyang, Huainan, Nanjing), south (Taizhou, Lishui, Jiande) and north (Taizhou, Yancheng). However, distinct transport routes were observed under the different air quality levels. During the heavy pollution period, the polluted air masses primarily came from the harbor regions, whereas during the light pollution period they were transported from the southeast (Taizhou) and the North China Plain.

Conditional probability function with uncertainty estimates

The conditional probability function (CPF) and the conditional bivariate probability function (CBPF) are widely used and useful tools that aid in identifying pollution sources in atmospheric research: they can tell us whether the wind from a particular direction increases or decreases pollutant concentrations. For this conclusion to be reliable, the observed decrease or increase must be established as statistically significant. Our literature search has shown that this is never done. Even more, we found that the majority of published analyses calculate CPF and CBPF from a relatively small number of measurements when the statistical fluctuations are large. The combination of these two facts is dangerous because it can drastically increase the likelihood of incorrect conclusion. To resolve this important issue, we have developed two independent methods for estimating the significance of the CPF and CBPF results. The methods, called binomial ratio and bootstrapping, are based on the construction of confidence intervals. We validated the methods on large and real data sets. We found them to be in a very good agreement and to have good coverage properties, which is the main criterion for the validity of confidence interval construction. The calculation and visualisation of CPF, CBPF and the associated confidence intervals was done in "CPFU", our freely available open-source software written in R. We also freely provide software that facilitates the (otherwise difficult) extraction of data files from the EPA website.

Advancing shipping NOx pollution estimation through a satellite-based approach

Estimating shipping nitrogen oxides (NOx) emissions and their associated ambient NO2 impacts is a complex and time-consuming task. In this study, a satellite-based ship pollution estimation model (SAT-SHIP) is developed to estimate regional shipping NOx emissions and their contribution to ambient NO2 concentrations in China. Unlike the traditional bottom-up approach, SAT-SHIP employs satellite observations with varying wind patterns to improve the top-down emission inversion methods for individual sectors amidst irregular emission plume signals. Through SAT-SHIP, shipping NOx emissions for 17 ports in China are estimated. The results show that SAT-SHIP performed comparably with the bottom-up approach, with an R2 value of 0.8. Additionally, SAT-SHIP reveals that the shipping sector in port areas contributes ∼21 and 11% to NO2 concentrations in the Yangtze River Delta and Pearl River Delta areas of China, respectively, which is consistent with the results from chemical transportation model simulations. This approach has practical implications for policymakers seeking to identify pollution sources and develop effective strategies to mitigate air pollution.

Evaluating the effectiveness of low-sulphur marine fuel regulations at improving urban ambient PM2.5 air quality: Source apportionment of PM2.5 at Canadian Atlantic and Pacific coast cities with implementation of the North American Emissions Control Area

Ambient fine size fraction particulate matter (PM2.5) sources were resolved by positive matrix factorization at two Canadian cities on the Atlantic and Pacific coast over the 2010-2016 period, corresponding to implementation of the North American Emissions Control Area (NA ECA) low-sulphur marine fuel regulations. Source types contributing to local PM2.5 concentrations were: ECA regulation-related (residual oil, anthropogenic sulphate), urban transportation and residential (gasoline, diesel, secondary nitrate, biomass burning, road dust/soil), industry (refinery, Pb-enriched), and largely natural (biogenic sulphate, sea salt). Anthropogenic sources accounted for approximately 80 % of PM2.5 mass over 2010-2016. Anthropogenic and biogenic sources of PM2.5-sulphate were separated and apportioned. Anthropogenic PM2.5-sulphate was approximately 2-3 times higher than biogenic PM2.5-sulphate prior to implementation of the NA ECA low-S marine fuel regulations, decreasing to 1-2 times higher after regulation implementation. Non-marine anthropogenic sources (gasoline, road dust, local industry factors) were shown to together contribute 38 % - 45 % of urban PM2.5. At both coastal cities, the residual oil and anthropogenic sulphate factors clearly reflected the effects of the low-S fuel regulations at reducing primary and secondary sulphur-related PM2.5 emissions. Comparing a pre-regulation and post-regulation period, residual oil combustion PM2.5 decreased by 0.24-0.25 μg/m3 (94%-95 % decrease) in both cities and anthropogenic sulphate PM2.5 decreased by 0.78 μg/m3 in Halifax (47 % decrease) and 0.71 μg/m3 in Burnaby (58 % decrease). Regulation-related PM2.5 across these factors decreased by approximately 1 μg/m3 after regulation implementation, providing a quantified lower estimate of the beneficial influence of the regulations on urban ambient PM2.5 concentrations. Further reductions in coastal city ambient PM2.5 may best consider air quality strategies that include multiple sources, including marine shipping and non-marine anthropogenic source types given this analysis found that marine vessel emissions remain an important source of urban ambient PM2.5.

An unneglected source to ambient brown carbon and VOCs at harbor area: LNG tractor truck

The ambient air quality of harbors area in Asia is commonly more polluted compared to other continents. The airborne pollutant is directly or indirectly related to a significant impact of traffic emissions. This study for the first time assessed the impacts on brown carbon (BrC) and volatile organic compounds (VOCs) from in-port liquid natural gas (LNG) tractor truck at harbor areas, via conducting real-time monitoring of VOCs characteristic and sampling for ambient air at a harbor (named as W harbor) in Shanghai, China, collecting emissions of in-port LNG tractor truck and miniCast in laboratory, as well as statistics of external container diesel trucks in the port for further validation. HPLC/DAD/Q-Tof MS was adopted for sample analysis. Results showed that many CHO compounds were associated with vehicle exhausts. Among of them, aliphatic CHO compounds with low degree of unsaturation were identified as fatty acids and fatty acid methyl esters extensively existing in fuel combustion emissions. And non-aliphatic CHO compounds characterized by low O/C ratios (<0.17) identified for the harbor air came from the emissions of in-port LNG power trucks with low-speed driving and idling. The ambient average non-methane total hydrocarbons (NMHC) concentration (0.59 ppm) at W harbor was much greater than that for other areas in Shanghai. The higher ratios of toluene/benzene (3.30) and m/p-xylene/ethylbenzene (3.11) observed at W harbor implied instead of external container diesel trucks, the dominating contributing of internal LNG tractor trucks to ambient VOCs cannot be neglected. This study concluded that LNG is not as clean as it was expected. The LNG-fueled vehicles can produce strong light-absorption chromophores as well as high concentration of VOCs.

Heavy vehicles' non-exhaust exhibits competitive contribution to PM2.5 compared with exhaust in port and nearby areas

Heavy port transportation networks are increasingly considered as significant contributors of PM2.5 pollution compared to vessels in recent decades. In addition, evidence points to the non-exhaust emission of port traffic as the real driver. This study linked PM2.5 concentrations to varied locations and traffic fleet characteristics in port area through filter sampling. The coupled emission ratio-positive matrix factorisation (ER-PMF) method resolves source factors by avoiding direct overlap from collinear sources. In the port central and entrance areas, freight delivery activity emissions including vehicle exhaust and non-exhaust particles, as well as induced road dust resuspension, accounted for nearly half of the total contribution (42.5%-49.9%). In particular, the contribution of non-exhaust from denser traffic with high proportion of trucks was competitive and equivalent to 52.3% of that from exhaust. Backward trajectory statistical models further interpreted the notably larger-scale coverage of non-exhaust emissions in the port's central area. The distribution of PM2.5 were interpolated within the scope of the port and nearby urban areas, displaying the potential contribution of non-exhaust within 1.15 μg/m3-4.68 μg/m3, slightly higher than the urban detections reported nearby. This study may provide useful insights into the increasing percentage of non-exhaust from trucks in ports and nearby urban areas and facilitate supplementary data collection on Euro-VII type-approval limit settings.

Impact of shipping emissions regulation on urban aerosol composition changes revealed by receptor and numerical modelling

Various shipping emissions controls have recently been implemented at both local and national scales. However, it is difficult to track the effect of these on PM2.5 levels, owing to the non-linear relationship that exists between changes in precursor emissions and PM components. Positive Matrix Factorisation (PMF) identifies that a switch to cleaner fuels since January 2020 results in considerable reductions in shipping-source-related PM2.5, especially sulphate aerosols and metals (V and Ni), not only at a port site but also at an urban background site. CMAQ sensitivity analysis reveals that the reduction of secondary inorganic aerosols (SIA) further extends to inland areas downwind from ports. In addition, mitigation of secondary organic aerosols (SOA) in coastal urban areas can be anticipated either from the results of receptor modelling or from CMAQ simulations. The results in this study show the possibility of obtaining human health benefits in coastal cities through shipping emission controls.

Dry-deposition of inorganic and organic nitrogen aerosols in Xiamen Bay: Fluxes, sources, and biogeochemical significance

Increased dry deposition of nitrogen aerosols (aerosol-N) as a result of anthropogenic emissions has caused large negative impacts on marine ecosystems. We monitored the number concentrations and sizes of inorganic nitrogen aerosols (aerosol-IN: NH₄⁺ and NO₃^-) and organic nitrogen aerosols (aerosol-ON: methylamine, dimethylamine, trimethylamine, ethylamine, diethylamine, and triethylamine) by single-particle aerosol mass spectrometry (SPAMS) during the warm season (WS) and cold season (CS) of 2013 and 2015 in Xiamen Bay. The mean hourly number concentration of aerosol-IN (874/h) overwhelmed that of aerosol-ON (103/h), accounting for 83.9 ± 16.1% of aerosol-N. More than 90% of aerosol-N was concentrated in the condensation mode (0.1-0.5 μm) and droplet mode (0.5-2.0 μm). Aerosol-IN was the main contributor (80.1-94.2%) to aerosol-N deposition. New production potentially supported by the ocean's external nitrogen supply provided aerosol-N input of 11.51-11.96 g C m^-2 yr^-1, which contributed 17.5-18.2% of total new production in the southern East China Sea. Four potential sources of aerosol-N were identified based on the results of positive matrix factorization analysis, including secondary formation (F1), biogenic source (F2), sea spray, soil dust, biomass burning (F3), and anthropogenic sources (F4). Aerosol-N concentrations in Xiamen Bay were mainly affected by the ocean air masses during the WS and inland air masses during the CS. The percentages of aerosol-N at each backward trajectory cluster showed that the inland air masses brought more aerosol-IN emitted from biomass burning, soil dust, and secondary formation sources, whereas the ocean air masses brought more aerosol-ON emitted from a marine biogenic source into Xiamen Bay. This study provides an example of determining the number concentrations and sizes of IN and ON in aerosols by SPAMS, and helps us further understand the dry deposition and sources of IN and ON in aerosols in Xiamen Bay.

Characterization and sources of trace elements in PM1 during autumn and winter in Qingdao, Northern China

Atmospheric sub-micrometer particles (PM₁, particles with an aerodynamic diameter ≤ 1.0 μm) monitoring in Qingdao, a coastal city in Northern China, was conducted for two consecutive years from November 1, 2018 to January 31, 2019 (hereafter referred to as OP_2018-2019) and from October 28, 2019 to January 20, 2020 (hereafter referred to as OP_2019-2020). The results showed that compared with OP_2018-2019, the concentrations of V, Ni, As, Pb, and Cd in PM₁ in OP_2019-2020 decreased by 61.9%, 31.4%, 49.2%, 25.4%, and 27.1%, respectively. For the indicators of ship emission sources, a significant reduction in V (73.3%) and Ni (22.1%) concentrations were observed after the implementation of the updated Domestic Emission Control Area (DECA 2.0) policy for ships since January 1, 2019 proposed by the Ministry of Transportation. This result demonstrated that the implementation of the DECA 2.0 policy had a significant effect on reducing ship emissions. The Field Emission Scanning Electron Microscope analysis identified the impact of ship emission sources, while the inconsistent distribution of V and Ni revealed other potential sources of Ni. The V/Ni ratios during the pre-policy and post-policy periods decreased by 40.7%. Along with the further implementation of the domestic coastal ship pollution control zone policy, V/Ni ratio should be cautiously used as a parameter for ship emission sources. The positive matrix factorization method identified five source factors: coal combustion/biomass burning (47.8%), crustal sources (21.2%), vehicle exhaust/road dust (15.1%), industrial emissions (11.1%), and ship emissions (4.9%). The contribution rates of ship emission sources before and after the DECA 2.0 policy were analyzed and found to be 5.6% and 3.4%. The potential source contribution factor analysis of As showed that the potential emission source areas were significantly reduced in OP_2019-2020, which might be related to the coal fired cleanup operations conducted in Beijing-Tianjin-Hebei and surrounding areas.

Heavy metal pollution levels, source apportionment and risk assessment in dust storms in key cities in Northwest China

In this study, the potential hazards of heavy metals in dust storms were investigated by collecting dust storm samples, measuring their heavy metal concentrations, and using index evaluation, spatial analysis, positive matrix factorization (PMF) model and risk assessment model. Heavy metals in dust storms were contaminated by anthropogenic sources leading to their concentrations being higher than the background values. The enrichment factors and geoaccumulation indices showed that the heavy metals came from both natural and anthropogenic sources, Cu, Ni, Zn and Pb are strongly influenced by anthropogenic sources. Heavy metals in dust storms were divided into four sources: Cu and Ni were attributed to industrial sources mainly from local mining and metal processing; Cr was mainly contributed by industrial sources related to industrial production such as coal combustion; Pb and Zn were mainly contributed by transportation sources; and Ti, V, Mn, Fe, and As were from natural and agricultural sources. The level of comprehensive ecological risk of heavy metals in dust storms were low, but there were moderate and above risks at individual sites. Both adults and children had the highest carcinogenic and non-carcinogenic risks from the ingestion route, and the risk for children was higher than that for adults.

Inter-comparison of chemical characteristics and source apportionment of PM2.5 at two harbors in the Philippines and Taiwan

This study inter-compared the concentration and chemical characteristics of PM_2.5 at two harbors in East Asia, and identified the potential sources of PM_2.5 and their contribution. Two sites located at the Kaohsiung (Taiwan) and Manila (the Philippines) Harbors were selected for simultaneous sampling of PM_2.5 in four seasons. The sampling of 24-h PM_2.5 was conducted for continuous seven days in each season. Water-soluble ions, metallic elements, carbonaceous content, anhydrosugars, and organic acids in PM_2.5 were analyzed to characterize their chemical fingerprints. Receptor modeling and trajectory simulation were further applied to resolve the source apportionment of PM_2.5. The results indicated that the Kaohsiung Harbor was highly influenced by long-range transport (LRT) of polluted air masses from Northeast Asia, while the Manila Harbor was mainly influenced by local emissions. Secondary inorganic aerosols were the most abundant ions in PM_2.5. Crustal elements dominated the metallic content of PM_2.5, but trace elements were mainly originated from anthropogenic sources. Higher concentrations of organic carbon (OC) than elemental carbon (EC) was found in PM_2.5, with secondary OC (SOC) dominant to the former. Levoglucosan in PM_2.5 at the Manila Harbor were superior to those at the Kaohsiung Harbor due to biomass burning surrounding the Manila Harbor. Additionally, high mass ratios of malonic and succinic acids (M/S) in PM_2.5 indicated the formation of SOAs. Overall, the ambient air quality of Manila Harbor was more polluted than Kaohsiung Harbor. The Kaohsiung Harbor was more severely affected by LRT of polluted air masses from Northeast Asia, while those toward the Manila Harbor came from the oceans. The major sources resolved by CMB and PMF models at the Kaohsiung Harbor were secondary aerosols, ironworks, incinerators, oceanic spray, and ship emissions, while those at the Manila Harbor were secondary aerosols, soil dust, biomass burning, ship emissions, and oceanic spray.

Source apportionment of PM2.5 and sulfate formation during the COVID-19 lockdown in a coastal city of southeast China

Revealing the changes in chemical compositions and sources of PM_2.5 is important for understanding aerosol chemistry and emission control strategies. High time-resolved characterization of water-soluble inorganic ions, elements, organic carbon (OC), and elemental carbon (EC) in PM_2.5 was conducted in a coastal city of southeast China during the COVID-19 pandemic. The results showed that the average concentration of PM_2.5 during the city lockdown (CLD) decreased from 46.2 μg m^-3 to 24.4 μg m^-3, lower than the same period in 2019 (PM_2.5: 37.1 μg m^-3). Concentrations of other air pollutants, such as SO₂, NO₂, PM₁₀, OC, EC, and BC, were also decreased by 27.3%-67.8% during the CLD, whereas O₃ increased by 28.1%. Although SO₂ decreased from 4.94 μg m^-3to 1.59 μg m^-3 during the CLD, the concentration of SO₄^2- (6.63 μg m^-3) was comparable to that (5.47 μg m^-3) during the non-lockdown period, which were attributed to the increase (16.0%) of sulfate oxidation rate (SOR). O_x (O₃+NO₂) was positively correlated with SO₄^2-, suggesting the impacts of photochemical oxidation. A good correlation (R² = 0.557) of SO₄^2- and Fe and Mn was found, indicating the transition-metal ion catalyzed oxidation. Based on positive matrix factorization (PMF) analysis, the contribution of secondary formation to PM_2.5 increased during the epidemic period, consisting with the increase of secondary organic carbon (SOC), while other primary sources including traffic, dust, and industry significantly decreased by 9%, 8.5%, and 8%, respectively. This study highlighted the comprehensive and nonlinear response of chemical compositions and formation mechanisms of PM_2.5 to anthropogenic emissions control under relatively clean conditions.

Ambient marine shipping emissions determined by vessel operation mode along the East China Sea

Marine shipping emissions exert important air quality and climate impacts. This study characterized the ambient pollutants predominant by emissions from a variety of marine vessel types near the mid-latitude East China Sea. Two discernible primary shipping emissions were identified by factorization analysis on detailed mass spectra of organic aerosol (OA), as emissions in maneuvering and cruise, highly linked with NO_x (and less oxidized OA, black carbon, BC) or CO (and more oxidized OA), respectively. Using radio-recorded quantities and activities of 3566 vessels mixed with slow and high-speed diesel engines, we found emission of NO_x or BC per vessel was positively correlated with vessel speed, while CO emission peaked at moderate speed. The approach here based on vessel operation mode directly linked the vessel activities to ambient concentrations of pollutants from marine shipping emission, and may synthesize the complex vessel types in shipping emission inventory.

Shipping and Air Quality in Italian Port Cities: State-of-the-Art Analysis of Available Results of Estimated Impacts

Populated coastal areas are exposed to emissions from harbour-related activities (ship traffic, loading/unloading, and internal vehicular traffic), posing public health issues and environmental pressures on climate. Due to the strategic geographical position of Italy and the high number of ports along coastlines, an increasing concern about maritime emissions from Italian harbours has been made explicit in the EU and IMO (International Maritime Organization, London, UK) agenda, also supporting the inclusion in a potential Mediterranean emission control area (MedECA). This work reviews the main available outcomes concerning shipping (and harbours’) contributions to local air quality, particularly in terms of concentration of particulate matter (PM) and gaseous pollutants (mainly nitrogen and sulphur oxides), in the main Italian hubs. Maritime emissions from literature and disaggregated emission inventories are discussed. Furthermore, estimated impacts to air quality, obtained with dispersion and receptor modeling approaches, which are the most commonly applied methodologies, are discussed. Results show a certain variability that suggests the necessity of harmonization among methods and input data in order to compare results. The analysis gives a picture of the effects of this pollution source, which could be useful for implementing effective mitigation strategies at a national level.

Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources

The submicron particulate matter (PM₁) and fine particulate matter (PM_2.5) are very important due to their greater adverse impacts on the natural environment and human health. In this study, the daily PM₁ and PM_2.5 samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin, China. The collected samples were analyzed for the carbonaceous fractions, inorganic ions, elemental species, and specific marker sugar species. The chemical characterization of PM₁ and PM_2.5 was based on their concentrations, compositions, and characteristic ratios (PM₁/PM_2.5, AE/CE, NO₃^-/SO₄^2-, OC/EC, SOC/OC, OM/TCA, K+/EC, levoglucosan/K⁺, V/Cu, and V/Ni). The average concentrations of PM₁ and PM_2.5 were 32.4 µg/m³ and 53.3 µg/m³, and PM₁ constituted 63% of PM_2.5 on average. The source apportionment of PM₁ and PM_2.5 by positive matrix factorization (PMF) model indicated the main sources of secondary aerosols (25% and 34%), biomass burning (17% and 20%), traffic emission (20% and 14%), and coal combustion (17% and 14%). The biomass burning factor involved agricultural fertilization and waste incineration. The biomass burning and primary biogenic contributions were determined by specific marker sugar species. The anthropogenic sources (combustion, secondary particle formation, etc) contributed significantly to PM₁ and PM_2.5, and the natural sources were more evident in PM_2.5. This work significantly contributes to the chemical characterization and source apportionment of PM₁ and PM_2.5 in near-port cities influenced by the diverse sources.

Chemical characterization and source apportionment of ambient nanoparticles: a case study in Hanoi, Vietnam

PM_0.1 has been believed to have adverse short- and long-term effects on human health. However, the information of PM_0.1 that is needed to fully evaluate its influence on human health and environment is still scarce in many developing countries. This is a comprehensive study on the levels, chemical compositions, and source apportionment of PM_0.1 conducted in Hanoi, Vietnam. Twenty-four-hour samples of PM_0.1 were collected during the dry season (November to December 2015) at a mixed site to get the information on mass concentrations and chemical compositions. Multiple linear regression analysis was utilized to investigate the simultaneous influence of meteorological factors on fluctuations in the daily levels of PM_0.1. Multiple linear regression models could explain about 50% of the variations of PM_0.1 concentrations, in which wind speed is the most important variable. The average concentrations of organic carbon (OC), elemental carbon (EC), water-soluble ions (Ca²⁺, K⁺, Mg²⁺, Na⁺, NH₄⁺, Cl^-, NO₃^-, SO₄^2-, C₂O₄^2-), and elements (Be, Al, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, Tl, Pb, Na, Fe, Mg, K, and Ca) were 2.77 ± 0.90 μg m^-3, 0.63 ± 0.28 μg m^-3, 0.88 ± 0.39 μg m^-3, and 0.05 ± 0.02 μg m^-3, accounting for 51.23 ± 9.32%, 11.22 ± 2.10%, 16.28 ± 2.67%, and 1.11 ± 0.94%, respectively. A positive matrix factorization model revealed the contributions of five major sources to the PM_0.1 mass including traffic (gasoline and diesel emissions, 46.28%), secondary emissions (31.18%), resident/commerce (12.23%), industry (6.05%), and road/construction (2.92%).

Source apportionment of hourly-resolved ambient volatile organic compounds: Influence of temporal resolution

High temporal-resolution VOC concentration data can provide detailed and important temporal variations of VOC species and emission sources, which is not possible when using coarse temporal-resolution data. In this study, we utilized the positive matrix factorization (PMF) model to conduct source apportionment of hourly concentrations of nineteen VOC species and CO measured at the Mong Kok air quality monitoring station, operated by the Hong Kong Environmental Protection Department, from January 2013 to December 2014. The PMF analysis of the hourly dataset (PMF_Hourly) identified five sources, including liquefied petroleum gas (LPG) (contribution of 45%), gasoline exhaust (21%), combustion (20%), biogenic emission (9%), and paint solvents (6%). The diurnal patterns of VOC emissions from identified sources are likely to be affected by the strength of emissions, variation of the planetary boundary layer height, and photochemical reactions. In addition, the PMF analyses of hourly and 24-hour averaged data of the hourly-resolved data (PMF_Hourly and PMF_Daily) were generally comparable, but the time series of VOC emissions from PMF_Hourly could not be well captured by PMF_Daily for two local VOC sources of gasoline exhaust and LPG. This study highlights the benefit of high temporal-resolution measurement data in apportioning VOC sources, hence providing critical information on VOC emission sources (e.g., diurnal variations) for controlling VOC emissions effectively.

Occurrence and risk assessment of benzothiazole, benzotriazole and benzenesulfonamide derivatives in airborne particulate matter from an industrial area in Spain

In this study we monitored benzothiazole (BTHs), benzotriazole (BTRs) and benzenesulfonamide (BSAs) derivatives in airborne particulate matter from four sampling sites near the port of Tarragona (Spain) over a one-year period. To do so, we developed a method based on ultrasound-assisted solvent extraction (USAE) followed by gas chromatography-mass spectrometry (GC-MS). We also studied concentrations of NO₂ and airborne particulate matter (PM_2.5 and PM_coarse) for a year. Our results showed NO₂ and PM_2.5 concentrations below the maximum average values established by the Europen Directive 2008/50/EC in the zone under study. Moreover, NO₂ values are directly proportional to changes in weather conditions and traffic emissions, while PM_coarse and PM_2.5 concentrations do not follow a clear trend as these may be generated from multiple sources (loading and unloading activities and traffic emissions). Regarding BTHs, BTRs and BSAs concentrations in particulate matter, the compounds found at the highest concentrations were 1-H-benzothiazole, 2-methylbenzothiazole, 2-chlorobenzothiazole, 1-H-benzotriazole, 4-methyl-1-H-benzotriazole, 2-(methylthio)-benzothiazole, 5-methyl-1-H-benzotriazole and bromobenzenesulfonamide with average concentrations ranging from 0.19 to 1.54 ng m^-3 in PM_coarse and from 0.09 to 0.61 ng m^-3 in PM_2.5. The remaining compounds were below the method quantification limits (MQLs) or were undetected in the samples analysed. Health risk values associated with the inhalation of the studied compounds were between 1.80 × 10^-3 and 1.27 × 10^-2 in the worst-exposure scenario.

Impact of harbour activities on local air quality: A review

Several harbour activities cause negative environmental impacts in the harbours' surrounding areas, namely the degradation of air quality. This paper intends to comprehensively review the status of the air quality measured in harbour areas. The published studies show a limited number of available air quality monitoring data in harbours areas, mostly located in Europe (71%). Measured concentrations of the main air pollutants were compiled and intercompared, for different countries worldwide allowing a large spatial representativeness. The higher NO₂ and PM₁₀ concentrations were found in Europe - ranging between 12 and 107 μg/m³ and 2-50 μg/m³, respectively, while the higher concentrations of PM_2.5 were found in Asia (25-70 μg/m³). In addition, the lower levels of SO₂ monitored in recent years suggest that current mitigation strategies adopted across Europe were very efficient in promoting the reduction of SO₂ concentrations. Part of the reviewed studies also estimated the contributions from ship emissions to PM concentration through the application of source apportionment methods, with an average of 5-15%. In some specific harbour areas in Asia, ships can contribute up to 7-26% to the local fine particulate matter concentrations. This review confirms that emissions from the maritime transport sector should be considered as a significant source of particulate matter in harbour areas, since this pollutant concentrations are frequently exceeding the established standard legal limit values. Therefore, the results from this review boost the implementation of mitigation measures, aiming to reduce, in particular, particulate matter emissions.

Influence of marine vessel emissions on the atmospheric PM2.5 in Japan's around the congested sea areas

In recent years, PM_2.5 concentrations in Japan have decreased as China's measures against the emission of air pollutants were strengthened and the subsequent transport of air pollutants to Japan decreased. On the other hand, along the coast of the Seto inland sea in Japan, the PM_2.5 concentration remains high. In this study, in order to evaluate the impact of air pollutants from marine vessels on PM_2.5 along the coast of the Seto inland sea, PM_2.5 was seasonally collected in the vicinity of a congested sea lane (Akashi Strait) in 2016 and 2017, and a receptor-source analysis was performed to determine the main components of the collected PM_2.5. In Japan's congested sea lane, the vanadium (V) concentration was very high and showed a strong correlation with the nickel (Ni) concentration. Also, the V/Ni ratio rose when the wind blew from the sea lane. Positive Matrix Factorization (PMF) analysis clarified that the contributions from marine vessel emissions to PM_2.5 at the current observation sites were 2.5-2.7 μg m^-3 (17.3-21.4%), and the marine vessel emissions were the main source of PM_2.5 along the coast of the Seto inland sea. Fuel oil regulations for marine vessels to be introduced in January 2020 are expected to improve the air quality of coastal areas.

Changes in the SO2 Level and PM2.5 Components in Shanghai Driven by Implementing the Ship Emission Control Policy

This study aims to understand the effect of the Domestic Emission Control Area (DECA) policy on ambient SO₂ and particle components in Shanghai. Online single particle analysis and SO₂ measurements from 2015 to 2017 were compared to analyze the long-term variations before and after the DECA policy. Our study showed that there was a significant decrease in SO₂ by 27-55% after the implementation of the DECA policy. The number fraction of ship-emitted particles increased along with the increase in ship traffic activity, but the particles tended to contain lower-vanadium content. The elemental carbon component decreased, while the organic carbon components increased after switching oil. One thousand and ninety four ship fuel oil samples were collected. The oil sample analysis confirmed the ambient particle results; sulfur content decreased in domestic ship heavy fuel oils from 2013 to 2018; in the low sulfur fuel oils used after the DECA policy, vanadium was still highly correlated with sulfur as it was in high-sulfur fuels. Our results suggested that heavy fuel oil is still a major part of the low-sulfur ship oils in use. The multiple-component control including organic pollutants regarding low sulfur fuel oils may be necessary for preventing air pollution from ship emissions.

Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution

Characteristics of the spatial and temporal distribution of air pollutants may reveal the cause of air pollution, especially for large regions where the anthropogenic pollutant emission is concentrated. This study addresses this issue by focusing on Shandong province, which has the highest air pollutant emissions in China. First, the spatial and temporal variation characteristics of the observed concentrations of conventional pollutants are analyzed in detail. The most prominent indicator of the problem (PM_2.5), was selected as the key analytical object. On the spatial scale, the Multivariate Moran model was used to identify factors affecting the spatial distribution of PM_2.5. On the time scale, wavelet analysis was used to explore the fluctuation characteristics of PM_2.5 at different time periods. Results show that there are significant regional differences in pollutant concentration within Shandong province. The concentration of particulate matter and gaseous pollutants in western and northern Shandong is significantly higher than eastern Shandong. The average concentrations of PM_2.5, PM₁₀, SO₂ and NO₂ were highest in winter and lowest in summer, whereas concentration of O₃ peaked in summer. For PM_2.5, the annual mean concentration has a significant spatial correlation with SO₂ emission, GDP per capita, population density and energy consumption per unit of GDP; in addition, the correlation between different regions and various indices is different. On the time scale, the fluctuation energy of PM_2.5 concentrated in Dezhou and Liaocheng is the strongest on December 18 and 19, 2015. The inversion temperature has a strong influence on the daily variation of PM_2.5 concentration. The formation and evolution of atmospheric pollution, therefore, can be explored by combining the temporal and spatial distribution of pollutants, providing a comprehensive analytical method for atmospheric pollution in different regions.

Spatio⁻Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998⁻2016

A comprehensive understanding of the relationships between PM2.5 concentration and socioeconomic factors provides new insight into environmental management decision-making for sustainable development. In order to identify the contributions of socioeconomic development to PM2.5, their spatial interaction and temporal variation of long time series are analyzed in this paper. Unary linear regression method, Spearman's rank and bivariate Moran's I methods were used to investigate spatio⁻temporal variations and relationships of socioeconomic factors and PM2.5 concentration in 31 provinces of China during the period of 1998⁻2016. Spatial spillover effect of PM2.5 concentration and the impact of socioeconomic factors on PM2.5 concentration were analyzed by spatial lag model. Results demonstrated that PM2.5 concentration in most provinces of China increased rapidly along with the increase of socioeconomic factors, while PM2.5 presented a slow growth trend in Southwest China and a descending trend in Northwest China along with the increase of socioeconomic factors. Long time series analysis revealed the relationships between PM2.5 concentration and four socioeconomic factors. PM2.5 concentration was significantly positive spatial correlated with GDP per capita, industrial added value and private car ownership, while urban population density appeared a negative spatial correlation since 2006. GDP per capita and industrial added values were the most important factors to increase PM2.5, followed by private car ownership and urban population density. The findings of the study revealed spatial spillover effects of PM2.5 between different provinces, and can provide a theoretical basis for sustainable development and environmental protection.

Spatial and temporal distribution, chemical characteristics, and sources of ambient particulate matter in the Beijing-Tianjin-Hebei region

Particulate matter (PM) pollution is severe in the Beijing-Tianjin-Hebei (BTH) region. Although the air quality has improved, the average PM_2.5 and PM₁₀ concentrations in 2016 were still higher than the National Ambient Air Quality Standard by 2.0 and 1.7 times, respectively. Using the empirical orthogonal function (EOF) method to analyze the spatial characteristics of its 13 cities, it was found that the BTH region could be categorized into four districts. The first district included Xingtai, Shijiazhuang, and Baoding; the second district included Handan, Hengshui, and Langfang; the third district included Beijing, Tangshan, Cangzhou, and Tianjin; and the fourth district included Qinhuangdao, Chengde, and Zhangjiakou. PM_2.5 samples were collected synchronously in five typical cities, and it was shown that the major chemical constituents of PM included organic carbon (OC), nitrate (NO₃^-), sulfate (SO₄^2-), ammonium (NH₄⁺), elemental carbon (EC), Si, Cl^-, Fe, Al, and Mg. The species with the highest contents were OC in the winter, SO₄^2- and NH₄⁺ in the summer, and NO₃^- in the spring. The highest concentrations of OC, NO₃^-, EC, Si, Cl^-, Al, and Mg were found in Baoding, and the highest concentrations of SO₄^2-, NH₄⁺, and Fe were found in Shijiazhuang. The sources of PM_2.5 were analyzed using the positive matrix factorization model. The major sources of PM_2.5 in the BTH region included coal combustion (10.9%-18.6%), secondary inorganic aerosols (35.4%-42.4%), vehicle emissions (10.6%-18.6%), soil/road dust (10.6%-23.6%), and industrial emissions (8.6%-18.2%).