Quantify Source Contributions Research Articles

Characterizing variability in geochemistry and mineralogy of western US dust sources

Dust events originate from multiple sources in arid and semi-arid regions, making it difficult to quantify source contributions. Dust geochemical/mineralogical composition, if the sources are sufficiently distinct, can be used to quantify the contributions from different sources. To test the viability of using geochemical and mineralogical measurements to separate dust-emitting sites, we used dust samples collected between 2018 and 2020 from ten National Wind Erosion Research Network (NWERN) sites that are representative of western United States (US) dust sources. Dust composition varied seasonally at many of the sites, but within-site variability was smaller than across-site variability, indicating that the geochemical signatures are robust over time. It was not possible to separate all the sites using commonly applied principal component analysis (PCA) and cluster analysis because of overlap in dust geochemistry. However, a linear discriminant analysis (LDA) successfully separated all sites based on their geochemistry, suggesting that LDA may prove useful for separating dust sources that cannot be separated using PCA or other methods. Further, an LDA based on mineralogical data separated most sites using only a limited number of mineral phases that were readily explained by the local geologic setting. Taken together, the geochemical and mineralogical measurements generated distinct signatures of dust emissions across NWERN sites. If expanded to include a broader range of sites across the western US, a library of geochemical and mineralogical data may serve as a basis to track and quantify dust contributions from these sources.

Historical co-enrichment, source attribution, and risk assessment of critical nutrients and heavy metal/metalloids in lake sediments: insights from Chaohu Lake, China.

In Chinese freshwater lakes, eutrophication often coincides with heavy metal/metalloids (HM/Ms) pollution, yet the coevolution of critical nutrients (P, S, Se) and HM/Ms (Cd, Hg, etc.) remains understudied. To address this gap, we conducted a sedimentary chemistry analysis on a 30cm-deep core, dating back approximately 200years, retrieved from Chaohu Lake, China. The age-depth model revealed a gradual increase in deposition rates over time. Notably, the concentrations and enrichment factors (EFs) of most target elements surged in the uppermost ~ 15cm layer, covering the period from 1953 to 2013, while both the concentrations and EFs in deeper layers remained relatively stable, except for Hg. This trend indicates a significant co-enrichment and near-synchronous increase in the levels and EFs of both nutrients and HM/Ms in the upper sediment layers since the mid-twentieth century. Anthropogenic factors were identified as the primary drivers of the enrichment of P, Se, Cd, Hg, Zn, and Te in the upper core, with their contributions also showing a coupled evolutionary trend over time. Conversely, geological activities governed the enrichment of elements in the lower half of the core. The gradual accumulation of anthropogenic Hg between the - 30 to - 15cm layers might be attributed to global Hg deposition resulting from the industrial revolution. The ecological risk index (RI) associated with HM/Ms loading has escalated rapidly over the past 50years, with Cd and Hg posing the greatest threats. Furthermore, the PMF model was applied to specifically quantify source contributions of these elements in the core, with anthropogenic and geogenic factors accounting for ~ 60 and ~ 40%, respectively. A good correlation (r2 = 0.87, p < 0.01) between the PMF and Ti-normalized method was observed, indicating their feasibility and cross-validation in source apportionment. Finally, we highlighted environment impact and health implications of the co-enrichment of nutrients and HM/Ms. This knowledge is crucial for developing strategies to protect freshwater ecosystems from the combined impacts of eutrophication and HM/Ms pollution, thereby promoting water environment and human health.

The reward and penalty for ozone pollution control caused by natural sources and regional transport: A case study in Guangdong province

Ground-level O3 pollution in the Pearl River Delta region (PRD) is closely related to anthropogenic, natural emissions and regional transport. However, the interactions among different sources and natural intervention in modulating anthropogenic management have not been comprehensively assessed. Here, the WRF-CMAQ-MEGAN modeling system was utilized to simulate an O3 episode over PRD. The integrated source apportionment method (ISAM) and brute-force top-down combined with factor separation approach (BF-TD-FSA) were applied to quantify source contributions, impacts of individual or multiple sources on O3, and decouple interactions among various emissions; additionally, based on ISAM, O3 isopleths visualized MDA8 O3 response of different source types to anthropogenic perturbations. ISAM concluded considerable MDA8 O3 contributions of regional transport in PRD/NPRD (non-PRD areas in Guangdong province) (38.8 %/35.7 %), followed by anthropogenic (32.7 %/24.8 %), BVOC (biogenic volatile organic compounds, 23.8 %/20.3 %) and SNO (soil NO, ∼4 %) emissions. Compared to concentrated anthropogenic contributions, widespread natural contributions were observed across their source areas and along the transport pathways. The BF-TD also revealed that regional transport had the largest impact (>90 μg m−3) on MDA8 O3 while anthropogenic and BVOC emissions greatly affected downwind PRD (64.5 and 7.7 μg m−3). Negative synergy between anthropogenic and natural emissions (especially BVOC emission) suggested potential natural-induced intensification of anthropogenic impact during O3 management. The MDA8 O3 isopleths further demonstrated significant BVOC-induced reward and regional transport-induced penalty for anthropogenic NOx (ANOx) emission control benefits, leading to additional maximum MDA8 O3 decrease and increase by −27.5 and 13.8 μg m−3 in polluted high-emission grids. The natural-induced reward effect could impose loose requirements on anthropogenic reduction (decreased by 13.3 %–17.7 %) if there were no regional transport-induced control penalty. It is advisable to prioritize ANOx control and seek collaboration on air quality management with neighboring provinces to maximize the natural-induced control reward and achieve desired targets with minimal human efforts.

Terrestrial inputs boost organic carbon accumulation in Mexican mangroves

Despite their ability to mitigate climate change by efficiently absorbing atmospheric carbon dioxide (CO2) and acting as natural long-term carbon sinks, mangrove ecosystems have faced several anthropogenic threats over the past century, resulting in a decline in the global mangrove cover. By using standardized methods and the most recent Bayesian tracer mixing models MixSIAR, this study aimed to quantify source contributions, burial rates, and stocks of organic carbon (Corg) and explore their temporal changes (∼100 years) in seven lead-210 dated sediment cores collected from three contrasting Mexican mangrove areas. The spatial variation in Corg burial rates and stocks in these blue carbon ecosystems primarily depended on the influence of local rivers, which controlled Corg sources and fluxes within the mangrove areas. The Corg burial rates in the cores ranged from 66 ± 16 to 400 ± 40 g m−2 yr−1. The Corg stocks ranged from 84.9 ± 0.7 to 255 ± 2 Mg ha−1 at 50 cm depth and from 137 ± 2 to 241 ± 4 Mg ha−1 at 1 m depth. The highest Corg burial rates and stocks were observed in cores from the carbonate platform of Yucatan and in cores with reduced river influence and high mangrove detritus inputs, in contrast to patterns identified from global databases. Over the past century, the rising trends in Corg burial rates and stocks in the study sites were primarily driven by enhanced inputs of fluvial-derived Corg and, in some cores, mangrove-derived Corg. Despite their decreasing extension, mangrove areas remained highly effective producers and sinks of Corg. Ongoing efforts to enhance the global database should continue, including mangrove area characteristics and reliable timescales to facilitate cross-comparison among studies.

Quantifying source contributions to ambient NH3 using Geo-AI with time lag and parcel tracking functions

Ambient ammonia (NH3) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH3′s properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH3 data monitored by the air quality stations. While there were other studies to predict NH3 and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH3 predictions and their contribution sources. This study represents a pioneering effort in the application of a novel geospatial-artificial intelligence (Geo-AI) base model with parcel tracking functions. This innovative approach seamlessly integrates various machine learning algorithms and geographic predictor variables to estimate NH3 concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH3 with domain knowledge. From 2016 to 2018, Taichung's hourly average NH3 values were predicted with total variance up to 96%. SHAP values revealed that waterbody, traffic and agriculture emissions were the most significant factors to affect NH3 concentrations in Taichung among all the characteristics. Our methodology is a vital first step for shaping future policies and regulations and is adaptable to regions with limited monitoring sites.

Organophosphate Esters in Building Materials from China: Levels, Sources, Emissions, and Preliminary Assessment of Human Exposure.

Source characteristics and health risks of indoor organophosphate esters (OPEs) are limited by the lack of knowledge on emission processes. This study attempted to integrate the contents and emissions of OPEs from indoor building materials to assess human health effects. Thirteen OPEs were investigated in 80 pieces of six categories of building materials. OPEs are ubiquitous in the building materials and ∑13OPE contents varied significantly (p < 0.05) from 72.8 ng/g (seam agent) to 109,900 ng/g (wallpaper). Emission characteristics of OPEs from the building materials were examined based on a microchamber method. Depending on the sample category, the observed initial area-specific emission rates of ∑13OPEs varied from 154 ng/m2/h (carpet) to 2760 ng/m2/h (wooden floorboard). Moreover, the emission rate model was developed to predict the release levels of individual OPEs, quantify source contributions, and assess associated exposure risks. Source apportionments of indoor OPEs exhibited heterogeneities in multiple environmental media. The joint OPE contribution of wallpaper and wooden floorboard to indoor dust was up to 94.8%, while latex paint and wooden floorboard were the main OPE contributors to indoor air (54.2%) and surface (76.1%), respectively. Risk assessment showed that the carcinogenic risks of tris(2-chloroethyl) phosphate (3.35 × 10-7) were close to the acceptable level (1 × 10-6) and deserved special attention.

A methodology for estimating indoor sources contributing to PM2.5.

Quantifying source contributions to indoor PM2.5 levels by indoor PM2.5 sources has been limited by the costs associated with chemical speciation analyses of indoor PM2.5 samples. Here, we propose a new methodology to estimate this contribution. We applied FUzzy SpatioTemporal Apportionment (FUSTA) to a database of indoor and outdoor PM2.5 concentrations in school classrooms plus surface meteorological data to determine the main spatiotemporal patterns (STPs) of PM2.5. We found four dominant STPs in outdoor PM2.5, and we denoted them as regional, overnight mix, traffic, and secondary PM2.5. For indoor PM2.5, we found the same four outdoor STPs plus another STP with a distinctive temporal evolution characteristic of indoor-generated PM2.5. Concentration peaks were evident for this indoor STP due to children's activities and classroom housekeeping, and there were minimum contributions on sundays when schools were closed. The average indoor-generated estimated contribution to PM2.5 was 5.7 μg m-3, which contributed to 17% of the total PM2.5, and if we consider only school hours, the respective figures are 8.1 μg m-3 and 22%. A cluster-wise indoor-outdoor PM2.5 regression was applied to estimate STP-specific infiltration factors (Finf) per school. The median and interquartile range (IQR) values for Finf are 0.83 [0.7-0.89], 0.76 [0.68-0.84], 0.72 [0.64-0.81], and 0.7 [0.62-0.9], for overnight mix, secondary, traffic, and regional sources, respectively. This cost-effective methodology can identify the indoor-generated contributions to indoor PM2.5, including their temporal variability.

Mixing Models With Multiple, Overlapping, or Incomplete End‐Members, Quantified Using Time Series of a Single Tracer

AbstractMixing models are used throughout earth and environmental science to quantify the relative contributions of sources to mixtures, based on chemical or isotopic tracers. Often, however, some end‐members are missing or their tracer distributions overlap, precluding the use of conventional mixing models. Here I show how these constraints can be overcome by exploiting the information contained in tracer time‐series fluctuations. This approach, ensemble end‐member mixing analysis (EEMMA), can potentially quantify many sources using a single tracer, even if their mean concentrations are indistinguishable. EEMMA can also quantify source contributions when some sources are unknown, and even infer the tracer time series of a missing source. Benchmark tests with synthetic data verify the reliability of this approach, thus expanding the range of mixing models that can be quantified using tracer time series. An R script is provided for the necessary calculations, including error propagation.

Riverine organic pollution source and yield from the whole Changjiang river network: Effects of urbanization under changing hydrology

Organic pollution in river systems is a key indicator of river water quality. Identifying organic pollution sources and quantifying source contributions are important in improving water quality planning and management. However, basin-scale patterns in riverine organic pollution sources and yields to predict water quality remain poorly understood. Here, we propose an Urban Disturbed Index (UDI) coupled with riverine hydrology to quantify the current and potential impacts of urbanization on riverine organic pollution at the basin scale. We identified the source contribution from urban point sources and non-point sources by using monthly riverine discharge and monthly monitored data in chemical oxygen demand (COD) and biological oxygen demand (BOD) concentrations from 2016 to 2018. There were significant spatial differences in the river CODMn and BOD5 fields across the whole basin, and the urban point-source contribution of CODMn and BOD5 increased from 19% to 79% and from 18% to 76%, respectively, from the river source area to the river mouth. The urban point-source yields of CODMn and BOD5 increased as the UDI increased. Our study suggests that basin-scale UDI together with riverine discharge determine the river organic pollution source and yield in the entire Changjiang River network. Our results are useful for riverine water quality planning and management.

Rapid increase in tropospheric ozone over Southeast Asia attributed to changes in precursor emission source regions and sectors

Observations indicate that tropospheric ozone (O3) concentrations over Southeast Asia have been increasing rapidly since the 1990s. Here, we quantify source contributions from geographical regions and emission sectors of the two distinct types of O3 precursors, i.e., nitrogen oxides (NOx) and volatile organic compounds (VOCs), to the increase in tropospheric O3 in Southeast Asia during 1990–2019 using an O3 source tagging technique implemented in a global chemistry-climate model. The results show that although local anthropogenic emission of NOx in Southeast Asia only contributes 18% of the annual averaged near-surface O3 concentration, the increase in local NOx emission dominates the increasing trend of O3 concentration in Southeast Asia, accounting for 107% of the regional averaged trend of 1.07 ppb decade−1. Increases in NOx emissions from East Asia and South Asia explain 29% of the increasing trend, but 9% is offset by the emission reduction in North America. The vertical O3 trends in the troposphere contributed by individual source regions are consistent with those near the surface. Ground transportation is responsible for 79% of the rapid O3 increase, followed by 39% contribution from international shipping. Because an increase in anthropogenic NOx emissions enhances the O3 production efficiency by VOCs, the increase in O3 concentrations in Southeast Asia is thereby largely contributed by methane and biogenic VOCs.

Source apportionment of fine particulate matter at a megacity in China, using an improved regularization supervised PMF model

The source apportionment of particulate matter plays an important role in solving the atmospheric particulate pollution. Positive matrix factorization (PMF) is a widely used source apportionment model. At present, high resolution online datasets are increasingly rich, but acquiring accurate and timely source apportionment results is still challenging. Integrating prior knowledge into modelling process is an effective solution and can yield reliable results. This study proposed an improved source apportionment method for the regularization supervised PMF model (RSPMF). This method leveraged actual source profile to guide factor profile for rapidly and automatically identifying source categories and quantifying source contributions. The results showed that the factor profile from RSPMF could be interpreted as seven factors and approach to actual source profile. Average source contributions were also an agreement between RSPMF and EPAPMF, including secondary nitrate (26 %, 27 %), secondary sulfate (23 %, 24 %), coal combustion (18 %, 18 %), vehicle exhaust (15 %, 15 %), biomass burning (10 %, 9 %), dust (5 %, 4 %), industrial emission (3 %, 3 %). The solutions of RSPMF also exhibited good generalizability during different episodes. This study reveals the superiority of supervised model, this model embeds prior knowledge into modelling process to guide model for obtaining more reliable results.

Quantification of the aeolian sand source in the Ulan Buh Desert using the sediment source fingerprinting (SSF) method within MixSIAR modelling framework

Identifying and quantifying source contributions of aeolian sands in desert where extensive dunes and paleo-lakes coexist is crucial to elucidate the formation mechanism of aeolian and lacustrine landforms in the earth system. In this study, we identified the aeolian sediment sources in the Ulan Buh Desert (UBD) using geomorphological and sedimentological methods, and further quantified the source contributions via the sediment source fingerprinting (SSF) method within MixSIAR modelling framework. Our results revealed that the mean contribution from each source to the UBD sediments were assessed to be 32.2 % for detrital deposits weathered and eroded from the Helan Mountains (HLM), Bayan Urals Mountains (BUM), and Langshan Mountains (LM), 26.2 % for aeolian and Gobi deposits from the adjacent Yamalik Desert (YMD) and Bayin Wendur Desert (BWD), 26.1 % for aeolian and Gobi sediments from the upwind Badain Jaran Desert (BJD), and 15.5 % for the paleo-lacustrine sediments in the UBD. The bulk fraction sands in the UBD are mainly contributed by detrital deposits from its surrounding mountains, whereas the fine sands are supplied by the upwind deserts of the UBD. As shown by the dunes migration rate (averaged 9.1 m/yr) and direction (averaged 118.3°) in the YMD, sands from upwind desert can be easily transported to the adjacent downwind desert, i.e., the UBD. Therefore, much attention should be paid to their role in sediment source contribution rather than ignore them for their limited area. Similarly, although the sediment source contribution from paleo-lacustrine deposits within the desert is relatively small, they served as a direct source for the UBD. The GOF evaluation demonstrated that the MixSIAR Bayesian model is an effective approach to aid aeolian sediment fingerprinting. In addition, we found that the aeolian sediments in the UBD is the combined results of wind and fluvial systems.

Border polje soil in Southwest China originated from non-karst areas: Evidence from geochemical fingerprint data

The purpose of this study was to provide scientific evidence to inform decisions on sediment mitigation strategies. A sediment fingerprinting approach was applied to identify soil sources for a border polje (BP) between karst (K) and non-karst (NK) areas in Southwest China. Three composite geochemical fingerprints, composed of basic, conservative, and high variability fingerprints, were used to quantify source contributions from two original groups (geology and land use), and three new classifications (two-, three-, and four-clusters) obtained using the k-means cluster analysis. The bi-plot, range test, tracer variation rate, and virtual mixture were used to test the reliability of each model configuration. The five classifications enabled the identification of > 85.7 % of the source samples. The two-cluster classification was consistent with the geology; the three-cluster divided the K into slope and depression, and the four-cluster further partitioned the NK into hydro-fluctuation belt of reservoirs (HFLRs) and highlands. The goodness of fit for these models was generally > 75 % and uncertainties were < 7.1 %. The HFLRs contributed > 90 % of soil parent material in the BP. The contribution of HL to the dry land near the NK in the BP was lower than that of HFLRs. Depressions contributed more than half to the dry land near the foothills of the limestone in the BP rim, followed by HFLRs. These findings highlight that the fluviatile sediments from NK areas significantly contribute to the BP. Hence, the identification of soil sources in the BP requires an evaluation from the perspective of hydrology, geology, and anthropogenic activities.

A joint method to quantify source contributions of heavy metals to ecological and human health risks in oasis farmland soil

AbstractSource identification and risk assessment of heavy metals are ongoing hot topics in current research, but few studies have been performed on the linkage mechanism between them. In the past, the amount of heavy metals discharged was the identification criterion for high‐risk source but failed to consider different toxicity of heavy metals in the risk level of each pollution source. Therefore, it is impossible to accurately determine high‐risk pollution source. For this situation, this study introduced a risk assessment model based on the source apportionment model, which can quantitatively analyze the source‐based risk. Meanwhile, pollution assessment indexes and a risk assessment model were applied to evaluate the levels of pollution and risk of heavy metals, showing that lead (Pb) caused relatively serious pollution and arsenic (As) generated the highest ecological risk and noncarcinogenic risk. Positive matrix factorization (PMF) model identified and quantified the sources of heavy metals (coal‐related activities source, mixed source of mining and traffic emissions, industrial activity source, agricultural source related to the application of agrochemicals) with the corresponding contributions of 42, 30, 26, and 2%, respectively. Then PMF was combined with potential ecological risk index and human health risk assessment model to quantify the risk from pollution sources, indicating that the coal‐related activities source was the largest pollution source (31–36%) that caused human health risks, while the mixed source of mining and transportation emissions posed the greatest threat (29%) to the ecosystem health. Therefore, both sources should be identified as the priority pollution sources.

Source apportionment of PM2.5 during different haze episodes by PMF and random forest method based on hourly measured atmospheric pollutant.

Hourly measured PM2.5-bound species, gases, and meteorological data were analyzed by the PMF receptor model to quantify source contributions, and by the random forest to estimate decisive factors of variations of PM2.5, sulfur oxidation ratio (SOR), and nitrogen oxidation ratio (NOR) during different haze episodes. PM2.5 variation was influenced by CO (17%), SO2 (19%), NH3 (12%), O3 (10%), air pressure (P, 9.9%), and temperature (T, 10%) during the whole period. SOR was determined by SO2 (15%), temperature (T, 9.8%), relative humidity (RHU, 15%), and pondus hydrogenii (pH, 35%), and NOR was influenced by NOx (19%), O3 (14%), NH3 (13%), and RHU (15%). Three types of pollution episodes were captured. Process I was characterized by high CO (contributing 40% of PM2.5 concentration variation estimated by the random forest) due to coal combustion for heating during winter in northern China. According to the PMF, coal combustion (32%) and secondary sources (38%) were both the most important contributors in the first stage, and then, when the RHU increased to above 80%, the highest contribution was from secondary sources (40%). Process II was during the Spring Festival and was characterized by 8.8 μg m-3 firework contribution. High SO2 during this process, especially on the CNY's Eve, was observed due to the firework displays, and SO2 gave a high contribution (24%) to PM2.5 variation. Process III showed high ions and high RHU in summer with sulfate and nitrate contributing 44% and 22%, respectively. Furthermore, meteorological parameters and NH3 play a key role on SOR and NOR.

Sr-Nd isotope and REE compositions of surface sediments from the three Gorges Reservoir: Implications for source identification and apportionment

Sediment deposition has already been considered to be a potential risk to the general running of the Three Gorges Reservoir (TGR). Identifying the sediment sources and their contributions to the TGR is of great significance for the sustainable management of reservoir operation. In this study, surface sediments of the TGR and its main tributaries were sampled in the flood (June to September), dry (November to next February) and even (March to May, October) seasons, 2017–2018, together with surrounding upland soils, and analyzed for Sr-Nd isotopes and rare earth elements (REE). The geochemical results showed that εNd(0) values and 87Sr/86Sr ratios of surface sediments from the TGR mainstream varied from − 12.21 to − 9.75 and from 0.720241 to 0.726655, respectively. All sediment and upland soil samples generally showed the LREE enrichment and significant Eu depletions. Three potential sources were identified for the TGR sediments and ranked in the order: regional source (adjacent tributaries) > distal source (upstream tributaries) > proximal source (upland soil), based on the Sr-Nd isotopes and REE parameters. Isotopic mixing model coupled with Monte-Carlo simulations was firstly applied to quantify source contributions to the TGR sediments among different seasons. Through Monte-Carlo model simulations, the regional source contributed approximately 40% to the TGR sediments during the flood and even seasons but approximately 50% during the dry season. The contribution from distal sources arrived at about 30% in the three seasons, and 30% from proximal sources during flood and even seasons and 20% in the dry season. Dam construction was the primary factor affecting the sediment deposition in the TGR, and it would further reduce sediment supply from the distal source with the operation of new increasing dams in the upper Yangtze River Basin. Hence, more attention should be paid to the regional sediment supply of the TGR in the future.

Identifying and quantifying source contributions of air quality contaminants during unconventional shale gas extraction

Abstract. The United States has experienced a sharp increase in unconventional natural gas (UNG) development due to the technological development of hydraulic fracturing. The objective of this study is to investigate the emissions at an active Marcellus Shale well pad at the Marcellus Shale Energy and Environment Laboratory (MSEEL) in Morgantown, West Virginia, USA. Using an ambient air monitoring laboratory, continuous sampling started in September 2015 during horizontal drilling and ended in February 2016 when wells were in production. High-resolution data were collected for the following air quality contaminants: volatile organic compounds (VOCs), ozone (O3), methane (CH4), nitrogen oxides (NO and NO2), and carbon dioxide (CO2), as well as typical meteorological parameters (wind speed and direction, temperature, relative humidity, and barometric pressure). Positive matrix factorization (PMF), a multivariate factor analysis tool, was used to identify possible sources of these pollutants (factor profiles) and determine the contribution of those sources to the air quality at the site. The results of the PMF analysis for well pad development phases indicate that there are three potential factor profiles impacting air quality at the site: natural gas, regional transport/photochemistry, and engine emissions. There is a significant contribution of pollutants during the horizontal drilling stage to the natural gas factor. The model outcomes show that there is an increasing contribution to the engine emission factor over different well pad drilling periods through production phases. Moreover, model results suggest that the regional transport/photochemistry factor is more pronounced during horizontal drilling and drillout due to limited emissions at the site.

Improving Inferences from Hydrological Isotope Techniques.

Plant water isotopic compositions are widely used to describe patterns of soils water uptake. Although valuable, the technique only provides relative uptake distributions, which can be misleading. Without information on total transpiration, the technique cannot address central questions on drought response, competition, and species coexistence.

Sources, variability and parameterizations of intra-city factors obtained from dispersion-normalized multi-time resolution factor analyses of PM2.5 in an urban environment

Ambient fine particulate matter (PM2.5) data of similar continuously monitored species at two air monitoring sites with different characteristics within the City of Toronto were used to gauge the intra-city variations in the PM composition over a largely concurrent period spanning two years. One location was <8 m from the side of a major highway while the other was an urban background location. For the first time, multi-time resolution factor analysis was applied to dispersion-normalized concentrations to identify and quantify source contributions while reducing the influence of local meteorology. These factors were particulate sulphate (pSO4), particulate nitrate (pNO3), secondary organic aerosols (SOA), crustal matter (CrM) that were common to both sites, a hydrocarbon-like organic matter (HOM) exclusive to the urban background site, three black carbon related factors (BC, BC-HOM at the highway site, and a brown carbon rich factor (BC-BrC) at the urban background site), biomass burning organic matter (BBOM) and brake dust (BD) factors exclusive to the highway site. The PM2.5 composition was different between these two locations, over only a 10 km distance. The sum of SOA, pSO4 and pNO3 at the urban background site averaged 57% of the PM2.5 mass while the same species represented 43% of the average PM2.5 mass at the highway site. Local or site-specific factors may be of greater interest for control policy design. Thus, regression analyses with potential explanatory, site-specific variables were performed for results from the highway site. Three model approaches were explored: multiple linear regression (MLR), regression with a generalized reduced gradient (GRG) algorithm, and a generalized additive model (GAM). GAM gave the largest fraction of variance for the locally-found factors at the highway site. Heavy-duty vehicles were most important for explaining the black carbon (BC and BC-HOM) factors. Light-duty vehicles were dominant for the brake dust (BD) factor. The auxiliary modelling for the local factors showed that the traffic-related factors likely originated along the main roadways at their respective sites while the more regional factors, − pSO4, pNO3, SOA, − had sources that were both regional and local in origin and with contributions that varied seasonally. These results will be useful in understanding ambient particulate matter sources on a city scale that will support air quality management planning.

Size distributions of source-specific risks of atmospheric heavy metals: An advanced method to quantify source contributions to size-segregated respiratory exposure

Heavy metals in size-segregated particulate matter (PM) were investigated in a Chinese megacity, and an advanced model was developed to quantify source-specific risks focusing on size-segregated respiratory exposure. Incremental lifetime cancer risk (ILCR) and non-cancer risk (hazard quotient: HQ) based on deposition concentrations of heavy metals displayed a peak at 4.7–5.8 µm. The percentage contributions to cancer risk were as follows: industrial emission (IE, 34%) > secondary and transport (ST, 29%) > resuspended dust (RD, 21%) > coal combustion (CC, 11%) > traffic emission (TE, 4%) during spring and summer (SS), and CC (31%) > ST (26%) > IE (21%) > RD (11%) ≈ TE (11%) during autumn and winter (AW). RD (41% of HQ during SS, 28% during AW) and IE (45% of HQ during SS, 35% during AW) dominated non-cancer risk. ILCR and HQ of CC were high at sizes 1.1–2.1 µm and 0.43–0.65 µm; those of RD were high at sizes > 3.3 µm; and those of IE were bimodal at fine (<2.1 µm) and coarse (>2.1 µm) sizes, respectively. Cancer risk was more susceptible to small particles than non-cancer risk, partly because higher ILCR was from CC, but higher HQ was attributed by RD.