Intraurban Spatiotemporal Variability of Ambient Air Pollutants across Metropolitan St. Louis

Ambient air monitoring networks have been established in the United States since the 1970s to comply with the Clean Air Act. The monitoring networks are primarily used to determine compliance but also provide substantive support to air quality management and air quality research including studies on health effects of air pollutants. The Roxana Air Quality Study (RAQS) was conducted at the fenceline of a petroleum refinery in Roxana, Illinois. In addition to providing insights into air pollutant impacts from the refinery, these measurements increased the St. Louis area monitoring network density for gaseous air toxics and fine particulate matter (PM2.5) speciation and thus provided an opportunity to examine intraurban spatiotemporal variability for these air quality parameters. This dissertation focused on exploring and assessing aspects of ambient air pollutant spatiotemporal variability in the St. Louis area from three progressively expanded spatial scales using a suite of methods and metrics. RAQS data were used to characterize air quality conditions in the immediate vicinity of the petroleum refinery. For example, PM2.5 lanthanoids were used to track impacts from refinery fluidized bed catalytic cracker emissions. RAQS air toxics data were interpreted by comparing to network data from the Blair Street station in the City of St. Louis which is a National Air Toxics Trends Station. Species were classified as being spatially homogeneous (similar between sites) or heterogeneous (different between sites) and in the latter case these differences were interpreted using surface winds data. For PM2.5 species, there were five concurrently operating sites in the St. Louis area - including the site in Roxana - which are either formally part of the national Chemical Speciation Network (CSN) or rigorously follow the CSN sampling and analytical protocols. This unusually large number of speciation sites for a region the size of St. Louis motivated a detailed examination of these data. Intraurban spatiotemporal variability for certain species was evaluated in the context of measurement error. For example, for species otherwise considered homogeneous, differential impacts from local point sources at different locations could be identified after comparing the observed day-to-day variations to those contributed by measurement error. In addition, source apportionment modeling was conducted using single- and multi-site datasets to assign measured PM2.5 mass to emission source categories. A suite of approaches were used to aid in the selection of an appropriate number of factors including metrics recently added to the US EPA Positive Matrix Factorization (EPA PMF) modeling software and the sensitivity of modeling results to perturbations on the measurement uncertainties

Compositional changes of PM2.5 in NE Spain during 2009–2018: a trend analysis of the chemical composition and source apportionment

In this work, time-series analyses of the chemical composition and source contributions of PM2.5 from an urban background station in Barcelona (BCN) and a rural background station in Montseny (MSY) in northeastern Spain from 2009 to 2018 were investigated and compared. A multisite positive matrix factorization analysis was used to compare the source contributions between the two stations, while the trends for both the chemical species and source contributions were studied using the Theil–Sen trend estimator. Between 2009 and 2018, both stations showed a statistically significant decrease in PM2.5 concentrations, which was driven by the downward trends of levels of chemical species and anthropogenic source contributions, mainly from heavy oil combustion, mixed combustion, industry, and secondary sulfate. These source contributions showed a continuous decrease over the study period, signifying the continuing success of mitigation strategies, although the trends of heavy oil combustion and secondary sulfate have flattened since 2016. Secondary nitrate also followed a significant decreasing trend in BCN, while secondary organic aerosols (SOA) very slightly decreased in MSY. The observed decreasing trends, in combination with the absence of a trend for the organic aerosols (OA) at both stations, resulted in an increase in the relative proportion of OA in PM2.5 by 12% in BCN and 9% in MSY, mostly from SOA, which increased by 7% in BCN and 4% in MSY. Thus, at the end of the study period, OA accounted for 40% and 50% of the annual mean PM2.5 at BCN and MSY, respectively. This might have relevant implications for air quality policies aiming at abating PM2.5 in the study region and for possible changes in toxicity of PM2.5 due to marked changes in composition and source apportionment

Oxidative potential in rural, suburban and city centre atmospheric environments in central Europe

Oxidative potential (OP) is an emerging health-related metric which integrates several physicochemical properties of particulate matter (PM) that are involved in the pathogenesis of the diseases resulting from exposure to PM. Daily PM2.5-fraction aerosol samples collected in the rural background of the Carpathian Basin and in the suburban area and centre of its largest city of Budapest in each season over 1 year were utilised to study the OP at the related locations for the first time. The samples were analysed for particulate matter mass, main carbonaceous species, levoglucosan and 20 chemical elements. The resulting data sets were subjected to positive matrix factorisation to derive the main aerosol sources. Biomass burning (BB), suspended dust, road traffic, oil combustion mixed with coal combustion and long-range transport, vehicle metal wear, and mixed industrial sources were identified. The OP of the sample extracts in simulated lung fluid was determined by ascorbic acid (AA) and dithiothreitol (DTT) assays. The comparison of the OP data sets revealed some differences in the sensitivities of the assays. In the heating period, both the OP and PM mass levels were higher than in spring and summer, but there was a clear misalignment between them. In addition, the heating period : non-heating period OP ratios in the urban locations were larger than for the rural background by factors of 2–4. The OP data sets were attributed to the main aerosol sources using multiple linear regression with the weighted least squares approach. The OP was unambiguously dominated by BB at all sampling locations in winter and autumn. The joint effects of motor vehicles involving the road traffic and vehicle metal wear played the most important role in summer and spring, with considerable contributions from oil combustion and resuspended dust. In winter, there is temporal coincidence between the most severe daily PM health limit exceedances in the whole Carpathian Basin and the chemical PM composition causing larger OP. Similarly, in spring and summer, there is a spatial coincidence in Budapest between the urban hotspots of OP-active aerosol constituents from traffic and the high population density in central quarters. These features offer possibilities for more efficient season-specific air quality regulations focusing on well-selected aerosol sources or experimentally determined OP, rather than on PM mass in general.</p

Source apportionment study on particulate air pollution in two high-altitude Bolivian cities: La Paz and El Alto

La Paz and El Alto are two fast-growing, high-altitude Bolivian cities forming the second-largest metropolitan area in the country. Located between 3200 and 4050 m a.s.l. (above sea level), these cities are home to a burgeoning population of approximately 1.8 million residents. The air quality in this conurbation is heavily influenced by urbanization; however, there are no comprehensive studies evaluating the sources of air pollution and their health impacts. Despite their proximity, the substantial variation in altitude, topography, and socioeconomic activities between La Paz and El Alto result in distinct sources, dynamics, and transport of particulate matter (PM). In this investigation, PM10 samples were collected at two urban background stations located in La Paz and El Alto between April 2016 and June 2017. The samples were later analyzed for a wide range of chemical species including numerous source tracers (OC, EC, water-soluble ions, sugar anhydrides, sugar alcohols, trace metals, and molecular organic species). The United States Environmental Protection Agency (U.S. EPA) Positive Matrix Factorization (PMF v.5.0) receptor model was employed for the source apportionment of PM10. This is one of the first source apportionment studies in South America that incorporates an extensive suite of organic markers, including levoglucosan, polycyclic aromatic hydrocarbons (PAHs), hopanes, and alkanes, alongside inorganic species. The multisite PMF resolved 11 main sources of PM. The largest annual contribution to PM10 came from the following two major sources: the ensemble of the four vehicular emissions sources (exhaust and non-exhaust), accountable for 35 % and 25 % of the measured PM in La Paz and El Alto, respectively; and dust, which contributed 20 % and 32 % to the total PM mass. Secondary aerosols accounted for 22 % (24 %) in La Paz (El Alto). Agricultural smoke resulting from biomass burning in the Bolivian lowlands and neighboring countries contributed to 9 % (8 %) of the total PM10 mass annually, increasing to 17 % (13 %) between August–October. Primary biogenic emissions were responsible for 13 % (7 %) of the measured PM10 mass. Additionally, a profile associated with open waste burning occurring from May to August was identified. Although this source contributed only to 2 % (5 %) of the total PM10 mass, it constitutes the second largest source of PAHs, which are compounds potentially hazardous to human health. Our analysis additionally resolved two different traffic-related factors, a lubricant source (not frequently identified), and a non-exhaust emissions source. Overall, this study demonstrates that PM10 concentrations in La Paz and El Alto region are predominantly influenced by a limited number of local sources. In conclusion, to improve air quality in both cities, efforts should primarily focus on addressing dust, traffic emissions, open waste burning, and biomass burning.</p

Equal abundance of summertime natural and wintertime anthropogenic Arctic organic aerosols

Organic aerosols in the Arctic are predominantly fuelled by anthropogenic sources in winter and natural sources in summer, according to observations from eight sites across the Arctic Aerosols play an important yet uncertain role in modulating the radiation balance of the sensitive Arctic atmosphere. Organic aerosol is one of the most abundant, yet least understood, fractions of the Arctic aerosol mass. Here we use data from eight observatories that represent the entire Arctic to reveal the annual cycles in anthropogenic and biogenic sources of organic aerosol. We show that during winter, the organic aerosol in the Arctic is dominated by anthropogenic emissions, mainly from Eurasia, which consist of both direct combustion emissions and long-range transported, aged pollution. In summer, the decreasing anthropogenic pollution is replaced by natural emissions. These include marine secondary, biogenic secondary and primary biological emissions, which have the potential to be important to Arctic climate by modifying the cloud condensation nuclei properties and acting as ice-nucleating particles. Their source strength or atmospheric processing is sensitive to nutrient availability, solar radiation, temperature and snow cover. Our results provide a comprehensive understanding of the current pan-Arctic organic aerosol, which can be used to support modelling efforts that aim to quantify the climate impacts of emissions in this sensitive region.Peer reviewe

기계학습과 수용모델을 이용한 초미세먼지 오염원 및 기여도의 시공간 분포 분석

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2023. 2. 김재영.직경 2.5 µm 이하의 입자상 물질인 초미세먼지는 대기중에 존재하며, 건강에 미치는 악영향으로 인해 수십 년 동안 세계적으로 관심의 대상이 되고 있는 대기오염물질이다. 초미세먼지를 효과적으로 관리하기 위해서는 다양한 시간과 공간에 대해 초미세먼지의 오염원 유형을 파악하고, 각 유형별 기여도를 정량화하는 것이 중요하다. 따라서, 초미세먼지의 오염원 추정은 핵심 과제로 다뤄져 왔으며, 통계학적 방법론을 적용해 오염원을 추정하는 수용모델이 많이 활용되고 있다. 본 연구에서는 초미세먼지의 세부 특성을 파악하기 위해 오염원 추정과 추정된 오염원의 시공간 분석을 수행하였으며, 이를 통해 효과적인 초미세먼지 관리 방안 마련에 중요한 정보를 제공하는 것을 목적으로 하였다. 오염원 유형 추정 연구를 위해, 두 가지 모델링이 수행되었다. 첫번째는 양행렬 인자 분석(Positive matrix factorization, PMF) 모델링으로, 이는 한 장소에서 초미세먼지의 오염원 유형을 구체적으로 추정하기 위해 활용되었다. 두번째는 베이지안 다변량 수용 모델링(Bayesian spatial multivariate receptor modelingm, BSMRM)으로, 이는 다수의 측정 지점으로부터 넓은 범위의 면적에 대해 주요 오염원 유형을 추정하기 위해 활용되었다. 또한, 기계학습 모델들을 활용하여 초미세먼지 오염원 유형 추정에 가장 중요한 자료로 활용되는 초미세먼지 화학성분 농도를 예측하였다. 기계학습 모델을 초미세먼지 화학성분 자료에 대해 활용가능한지를 검토하였고, 이를 통해 초미세먼지 화학성분 자료의 무결성을 향상시키고자 하였다. PMF 모델링을 통해, 대한민국 시흥시의 초미세먼지 오염원 유형 10가지를 도출하였다. 이는 각각 2차 생성 질산염(24.3%), 2차 생성 황산염(18.8%), 이동 오염원(18.8%), 난방연소(12.6%), 생물체 연소(11.8%), 석탄 연소(3.6%), 중유 관련 산업 오염원(1.8%), 제련 관련 산업 오염원(4.0%), 해염 입자(2.7%), 토양(1.7%)였다. 도출된 오염원 유형별로, 초미세먼지 호흡에 따른 건강 영향을 평가하였다. 석탄 연소, 중유 관련 산업 오염원, 이동 오염원의 초미세먼지 기여도는 낮았지만, 이로 인한 발암 위해도는 10E-6 이상으로 나타났다. 따라서, 초미세먼지의 질량농도 감축 중심의 대응만이 아닌, 오염원별 건강영향 중심의 대응이 요구된다. 기계학습 모델의 초미세먼지 화학성분 예측 능력을 평가하기 위해 4가지 기계학습 모델에 대해 입력 자료 수준, 예측 대상 성분, 입력 자료 기간, 입력 자료의 결측 비율, 자료 대상 지역을 변화하며 예측 정확도를 비교 평가하였다. GAIN(Generative Adversarial Imputation Network), FCDNN(Fully Connected Deep Neural Network), Random forest(RF), kNN(k-nearest neighboring) 모델의 4가지 기계학습 모델을 한국의 3개 지역(서울, 울산, 백령)의 2016년부터 2018년까지의 초미세먼지 화학 성분 자료에 대해 적용하여 농도를 예측하였다. 예측값과 관측값 사이의 결정계수를 통해 정확도를 비교한 결과, 예측 정확도는 GAIN이 가장 높았고, FCDNN, RF 또는 kNN 순서로 나타났다. 입력 자료의 결측률이 20%에서 80%까지 증가함에 따라 예측 정확도는 모든 모델에서 감소하였으나, 비지도 기계학습 모델인 GAIN과 kNN에서 감소 폭이 더 크게 나타났다. 입력 자료의 기간이 길어질수록, 딥러닝 모델인 GAIN과 FCDNN이 다른 두 모델인 RF와 kNN보다 예측 정확도 증가 폭이 더 컸다. 예측 대상 지역별로는, 자체 배출원이 많은 울산의 경우가 예측 정확도가 가장 낮게 나타났고, 자체 배출원의 영향이 거의 없는 백령도의 경우 예측 정확도가 가장 높게 나타났다. 대상 성분별로는 이온 성분이 예측 정확도가 높게 나타났고, 미량원소 성분은 예측 정확도가 낮았다. 본 연구는 기계학습 모델의 예측 정확도를 다양한 실험 조건에 따라 평가하여 대기오염 분야에서의 기계학습 모델의 적용 가능성을 평가했다. 베이지안 다변량 수용 모델링(BSMRM)을 통해서는 8개의 관측 지점 자료를 통해 우리나라의 주요 초미세먼지 오염원 5가지를 도출하고, 각각 오염원 유형별 기여도를 우리나라 전체에 대한 공간 분포를 추정하였다. 5가지 오염원은 각각 2차 질산염, 2차 황산염, 자동차 배출, 산업 오염원, 해염 입자였다. 각 오염원 유형별 일평균 기여도 농도를 지도에 공간적으로 표현할 수 있었다. 또한, BSMRM을 통해 예측한 오염원 유형별 기여도의 타당성 검토를 위해 테스트 사이트(안산, 대전, 광주)의 자료는 각각 제외된 모델링을 수행하여 결과를 서로 비교하여 모델의 정확도를 확인하였다. 이처럼 공간적으로 추정된 오염원 유형 기여도는 초미세먼지 화학성분을 측정하지 않는 도시에서 초미세먼지 대응 방안을 수립하는데 큰 도움이 될 수 있다. 즉, 8개의 측정 자료만으로 우리나라 전체에 대해 예측한 결과를 통해, 측정 지점이 없는 모든 도시에 대해 추정이 가능하였으며, 이 결과는 건강 영향 평가와 같은 추가 연구에도 활용될 수 있다.Particulate matter less than 2.5 micrometers (PM2.5) has been a pollutant of interest globally for more than decades, owing to its adverse health effects. For developing effective PM2.5 management strategies, it is crucial to identify their sources and quantify how much they contribute to ambient PM2.5 concentrations in time and space. Source apportionment is the key to identifying the characteristics of PM2.5. Receptor modeling is widely used to identify PM2.5 sources as a statistical method of source apportionment. The chemical constituents of PM2.5 were used as input data for receptor modeling. Therefore, this study aimed to investigate the characteristics of PM2.5 using models of source apportionment and spatiotemporal analysis for effective management strategies. Two types of modeling were performed for the source apportionment study. The first is positive matrix factorization modeling, which identifies a specific source type and its contributions to PM2.5 from one site. The second is Bayesian spatial multivariate receptor modeling, which derives major sources and their contributions to PM2.5 from multiple monitoring sites. In addition, machine learning models were used to predict the concentrations of PM2.5, which are important data for receptor modeling. Machine learning models that can be used to increase data integrity and applicability to PM2.5 data were assessed. The sources of PM2.5 and their contributions in Siheung, South Korea, were identified using positive matrix factorization modeling. These 10 sources were secondary nitrate (24.3%), secondary sulfate (18.8%), traffic (18.8%), combustion for heating (12.6%), biomass burning (11.8%), coal combustion (3.6%), heavy oil industry (1.8%), smelting industry (4.0%), sea salt (2.7%), and soil (1.7%). Based on the derived sources, the carcinogenic and non-carcinogenic health risks due to PM2.5 inhalation were estimated. The contribution to PM2.5 mass concentration was low for coal combustion, heavy oil industry, and traffic sources but exceeded the benchmark carcinogenic health risk value (1E-06). Therefore, countermeasures on PM2.5 emission sources should be performed based on the PM2.5 mass concentration and health risks. The feature extraction capabilities of the four machine learning models to predict the chemical constituents of PM2.5 were assessed by comparing the prediction accuracy depending on input variables, target constituents for prediction, available period, missing ratios of input data, and study sites. The concentrations of PM2.5 constituents were predicted at three sites (Seoul, Ulsan, and Baengnyeong) in South Korea between 2016 and 2018, using four machine learning models: generative adversarial imputation network (GAIN), fully connected deep neural network (FCDNN), random forest (RF), and k-nearest neighbor (kNN). The prediction accuracy identified by the coefficient of determination (R2) between the prediction and observation was highest in GAIN, followed by FCDNN, RF, and kNN. As the missing ratios (20, 40, 60, and 80%) of the input data increased, the prediction accuracy decreased in the four models and was more noticeable in GAIN and kNN, which are unsupervised models. As the input data period increased, the two deep learning models, GAIN and DNN, had better applicability than the other models, RF and kNN. The study sites with more emission sources exhibited lower prediction accuracy, resulting in the highest R2 in the BR island and the lowest in Ulsan. Among the target constituent groups, ions and trace elements were predicted to have the highest and lowest R2, respectively. This study demonstrated that machine learning models can be extended for further air pollution studies depending on model features, required performance, and experimental conditions, such as data availability and time constraints. The spatial distributions of five PM2.5 sources in South Korea were estimated using Bayesian spatial multivariate receptor modeling. Secondary nitrate, secondary sulfate, motor vehicle emissions, industry, and sea salts were determined to be significant contributors to ambient PM2.5 concentrations in South Korea. The spatial surface of the daily average contribution for each source in South Korea was derived from measurement data from the eight monitoring sites. The source contributions predicted by the BSMRM were also validated using held-out data from a test site (such as Ansan, Daejeon, and Gwangju). These predicted source contributions can aid in developing effective PM2.5 control strategies in cities where no speciated PM2.5 monitoring stations are available. They can also be utilized as source-specific exposures in health effect studies, even in cities where no monitoring stations are available.CHAPTER 1. INTRODUCTION 1 1.1. Background 1 1.2. Objectives 4 1.3. Dissertation structure 5 References 7 CHAPTER 2. LITERATURE REVIEW 10 2.1. Source apportionment and receptor modeling of PM2.5 10 2.2. Toxicity and health risk of assessment PM2.5 21 2.3. Machine learning approaches in prediction of PM2.5 31 2.4. Bayesian approach in source apportionment 41 References 54 CHAPTER 3. SOURCE APPORTIONMENT OF PM2.5 USING PMF MODEL AND HEALTH RISK ASSESSMENT BY INHALATION 69 3.1. Introduction 69 3.2. Materials and methods 72 3.2.1 Study site, sampling, and analysis 72 3.2.2 Positive matrix factorization (PMF) modeling and combined analysis with meteorological data 76 3.2.3 Health risk assessment 80 3.3. Results and discussion 85 3.3.1 PM2.5 mass concentration and chemical speciation 85 3.3.2 Source apportionment of PM2.5 by PMF modeling 89 3.3.3 Carcinogenic and non-carcinogenic health risks 94 3.3.4 Probable source areas or directions 103 3.4. Summary 106 References 107 CHAPTER 4. FEATURE EXTRACTION AND PREDICTION OF PM2.5 CHEMICAL CONSTITUENTS USING MACHINE LEARNING MODELS 120 4.1. Introduction 120 4.2. Materials and methods 124 4.2.1. Study Sites and Data Collection 124 4.2.2. Machine Learning Models and Hyperparameter Optimization 127 4.2.3. Prediction Scenarios 131 4.2.4. Model Validation and Error Estimation 133 4.3. Results and discussion 134 4.3.1. Hyperparameter Optimization 134 4.3.2. Prediction Results for Scenario #1 135 4.3.3. Prediction Results for Scenario #2 157 4.3.4. Features and Performance of Four ML Models 164 4.4. Summary 166 Data Availability 167 Code Availability 167 References 168 CHAPTER 5. BAYESIAN SPATIAL MULTIVARIATE RECEPTOR MODELING FOR SPATIOTEMPORAL ANALYSIS OF PM2.5 SOURCES 175 5.1. Introduction 175 5.2. Materials and methods 180 5.2.1 Air pollution data 180 5.2.2 Bayesian spatial multivariate receptor modeling (BSMRM) 183 5.2.3 Application of BSMRM to Korea PM2.5 speciation data 185 5.3. Results and discussion 189 5.3.1 Bayesian spatial multivariate receptor modeling (BSMRM) results 189 5.3.2 Model validation 196 5.3.3 Spatial distribution of each source in South Korea 204 5.4. Summary 207 References 208 CHAPTER 6. CONCLUSIONS AND FUTURE WORK 214 6.1. Conclusions 214 6.2. Future work 218 국문 초록(ABSTRACT IN KOREAN) 219박

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat

Vulnerability of Transboundary River Basins in a Changing Climate: A Case Study of the Saskatchewan River Basin

About half of the Earth’s land surface is covered by transboundary water resources. Approximately 40 percent of the world’s population relies on water resources crossing political borders. Within transboundary river basins, allocating these limited and often depleting resources to states is challenging due to various, and often conflicting interests of stakeholders. Treaties and River Basin Organizations (RBOs) provide the primary means of cooperation between states, building institutional capacity, and lowering the likelihood of hydropolitical tensions. A resilient transboundary river system should be able to tolerate the pressures from different stressors to provide a reliable source of water. However, geopolitical, socio-economic, and biophysical stressors threaten the governance of these basins. Climate change is one of the biophysical stressors which is likely to increasingly challenge transboundary river systems. A thorough understanding of climate-change-induced vulnerabilities of a transboundary system, therefore, can help decision and policy makers to plan for adaptive measures to avoid hydropolitical tensions. The Saskatchewan River Basin, located in western Canada and shared amongst the three Canadian provinces of Alberta, Saskatchewan, and Manitoba and also the American state of Montana, is used as a case study. In particular, this thesis assesses the viability of the 1969 Master Agreement on Apportionment that provides the basis for water allocation of eastward flowing interprovincial streams in face of deep uncertainty around future climate change. To this end, a vulnerability assessment methodology consisting of three main components is proposed. First a large set of plausible weather scenarios is generated by perturbing important features of climate including winter precipitation, summer precipitation, annual temperature, and the annual number of dry days. Second, the weather scenarios are fed into a conceptual hydrological model calibrated to historical record to generate a wide range of plausible future streamflow scenarios. Third, the streamflow scenarios are used as input to a water resources management model that distributes the water throughout the transboundary river system. Results show a moderate risk of failure in the southern part of the basin in meeting the criteria established in the apportionment agreement under certain possible changes in climate regime of the region. The risk of not meeting the minimum flow is accompanied by major deficits to irrigation and non-irrigation demands as well as minimum environmental flows. A lower risk is observed in other parts of the basin, mainly due to lower water usage and abstraction

Spatial origin analysis on atmospheric bulk deposition of polycyclic aromatic hydrocarbons in Shanghai

Atmospheric deposition of polycyclic aromatic hydrocarbons (PAHs) onto soil threatens terrestrial ecosystem. To locate potential source areas geographically, a total of 139 atmospheric bulk deposition samples were collected during 2012–2019 at eight sites in Shanghai and its surrounding areas. A multisite joint location method was developed for the first time to locate potential source areas of atmospheric PAHs based on an enhanced three dimensional concentration weighted trajectory model. The method considered spatial and temporal variations of atmospheric boundary layer height and homogenized all results over the eight sites via geometric mean. Regional transport was an important contributor of PAH atmospheric deposition while massive local emissions may disturb the identification of potential source areas. Northwesterly winds were associated with elevated deposition fluxes. Potential source areas were identified by the multisite joint location method and included Hebei, Tianjin, Shandong and Jiangsu to the north, and Anhui to the west of Shanghai. PM and SO2 data from the national ground monitoring stations confirmed the identified source areas of deposited PAHs in Shanghai