Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada’s Regional Air Quality Deterministic Prediction System

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr.

Implications: This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.     

Socio-environmental consideration of phosphorus flows in the urban sanitation chain of contrasting cities

Understanding how cities can transform organic waste into a valuable resource is critical to urban sustainability. The capture and recycling of phosphorus (P), and other essential nutrients, from human excreta is particularly important as an alternative organic fertilizer source for agriculture. However, the complex set of socio-environmental factors influencing urban human excreta management is not yet sufficiently integrated into sustainable P research. Here, we synthesize information about the pathways P can take through urban sanitation systems along with barriers and facilitators to P recycling across cities. We examine five case study cities by using a sanitation chains approach: Accra, Ghana; Buenos Aires, Argentina; Beijing, China; Baltimore, USA; and London, England. Our cross-city comparison shows that London and Baltimore recycle a larger percentage of P from human excreta back to agricultural lands than other cities, and that there is a large diversity in socio-environmental factors that affect the patterns of recycling observed across cities. Our research highlights conditions that may be “necessary but not sufficient” for P recycling, including access to capital resources. Path dependencies of large sanitation infrastructure investments in the Global North contrast with rapidly urbanizing cities in the Global South, which present opportunities for alternative sanitation development pathways. Understanding such city-specific social and environmental barriers to P recycling options could help address multiple interacting societal objectives related to sanitation and provide options for satisfying global agricultural nutrient demand.

     

The impact of global change on economic values of water for Public Irrigation Schemes at the São Francisco River Basin in Brazil

Economic values of water for the main Public Irrigation Schemes in the sub-middle region of the São Francisco River Basin, in northeastern Brazil, are determined in this study using an integration of a global agro-economic land and water use (MAgPIE) with a local economic model (Positive Mathematical Programming). As in the latter, the water values depend on the crops grown, and as Brazilian agriculture is strongly influenced by the global market, we used a regionalized version of the global model adapted to the region in order to simulate the crop land use, which is in turn determined by changes in global demand, trade barriers, and climate. The allocation of sugarcane and fruit crops projected with climate change by the global model, showed an impact on the average yields and on the water costs in the main schemes resulting in changes in the water values locally. The economic values for all schemes in the baseline year were higher than the water prices established for agricultural use in the basin. In the future, these water values will be higher in all the schemes. The highest water values currently and in the future were identified in municipalities with a significant proportion of area growing irrigated sugarcane. Being aware of current water values of each user in a baseline year and in a projected future under global climate and socioeconomic changes, decision makers should improve water allocation policies at local scale, in order to avoid conflicts and unsustainable development in the future.     

Industrial pollution,spatial stigma and economic decline: the case of Asopos river basin through the lens of local small business owners

This paper explores the notion of environmentally induced spatial stigma through an analysis of data from interviews across public attitudes to pollution within the Asopos river basin in central Greece. The area has a 40 year plus history of legal and illicit industrial waste disposal and public debate on the associated environmental degradation. The study focuses on the perceptions and beliefs of a sector of the community likely to be directly and negatively affected by stigma, that is small business owners in the tourism and hospitality sector. The qualitative analysis explores awareness and viewpoints on environmental degradation and water quality within the local context, implications for the local economy and the individual's own enterprise, views on industrial environmental management as well as corporate responsibility and future prospects for the environmental problems of Asopos. Findings reveal a noticeable variation in views on industrial pollution and ecosystem deterioration among the respondents, but overall a strong environmentally induced stigmatization of the area. They also uncover an information asymmetry and lack of credible commitment by government bodies and industry members in disclosing accurate information, a situation likely to increase speculation and uncertainty within the community. The paper concludes by addressing implications of the findings to policy-making and managerial considerations, along with future research perspectives which aim to increase considerations of sustainability aspects for local development.     

Methane biofiltration in the presence of ethanol vapor under steady and transient state conditions: an experimental study

Methane (CH₄) removal in the presence of ethanol vapors was performed by a stone-based bed and a hybrid packing biofilter in parallel. In the absence of ethanol, a methane removal efficiency of 55 ± 1% was obtained for both biofilters under similar CH₄ inlet load (IL) of 13 ± 0.5 g_CH4 m^?3 h^?1 and an empty bed residence time (EBRT) of 6 min. The results proved the key role of the bottom section in both biofilters for simultaneous removal of CH₄ and ethanol. Ethanol vapor was completely eliminated in the bottom sections for an ethanol IL variation between 1 and 11 g_ethanol m^?3 h^?1. Ethanol absorption and accumulation in the biofilm phase as well as ethanol conversion to CO₂ contributed to ethanol removal efficiency of 100%. In the presence of ethanol vapor, CO₂ productions in the bottom section increased almost fourfold in both biofilters. The ethanol concentration in the leachate of the biofilter exceeding 2200 g_ethanol m^?3 _leachate in both biofilters demonstrated the excess accumulation of ethanol in the biofilm phase. The biofilters responded quickly to an ethanol shock load followed by a starvation with 20% decrease of their performance. The return to normal operations in both biofilters after the transient conditions took less than 5 days. Unlike the hybrid packing biofilter, excess pressure drop (up to 1.9 cmH₂O m^?1) was an important concern for the stone bed biofilter. The biomass accumulation in the bottom section of the stone bed biofilter contributed to 80% of the total pressure drop. However, the 14-day starvation reduced the pressure drop to 0.25 cmH₂O m^?1.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

Water resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions (GAMUT) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH, nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.     

Characterization of Fine Particulate Matter Produced by Combustion of Residual Fuel Oil

ABSTRACT

Combustion experiments were carried out on four different residual fuel oils in a 732-kW boiler. PM emission samples were separated aerodynamically by a cyclone into fractions that were nominally less than and greater than 2.5 |j.m in diameter. However, examination of several of the samples by computer-controlled scanning electron microscopy (CCSEM) revealed that part of the PM_2.5 fraction consists of carbonaceous cenospheres and vesicular particles that range up to 10 |j.m in diameter. X-ray absorption fine structure (XAFS) spectroscopy data were obtained at the S, V, Ni, Fe, Cu, Zn, and As K-edges and at the Pb L-edge. Deconvolution of the X-ray absorption near edge structure (XANES) region of the S spectra established that the dominant molecular forms of S present were sulfate (26-84% of total S) and thiophene (13-39% of total S). Sulfate was greater in the PM_2.5 samples than in the PM₂₅₊ samples. Inorganic sulfides and elemental sulfur were present in lower percentages. The Ni XANES spectra from all of the samples agreed fairly well with that of NiSO₄, while most of the V spectra closely resembled that of vanadyl sulfate (VO?SO₄?xH₂O). The other metals investigated (i.e., Fe, Cu, Zn, and Pb) also were present predominantly as sulfates. Arsenic was present as an arsen-ate (As⁺⁵). X-ray diffraction patterns of the PM_2.5 fraction exhibit sharp lines due to sulfate compounds (Zn, V, Ni, Ca, etc.) superimposed on broad peaks due to amorphous carbons. All of the samples contain a significant organic component, with the loss on ignition (LOI) ranging from 64 to 87% for the PM_2.5 fraction and from 88 to 97% for the PM_2.5+ fraction. Based on ¹³C nuclear magnetic resonance (NMR) analysis, the carbon is predominantly condensed in graphitic structures. Aliphatic structure was detected in only one of seven samples examined.     

Gas-phase and semivolatile organic emissions from a modern nonroad diesel engine equipped with advanced aftertreatment

ABSTRACT

U.S. Tier 4 Final and Euro Stage IV and V regulations for nonroad compression-ignition engines have led to the development of exhaust aftertreatment technologies optimized for nonroad engines and duty cycles. In this study, several aftertreatment configurations consisting of state-of-the-art diesel oxidation catalysts (DOCs), diesel particulate filters (DPFs), copper (Cu) zeolite– and vanadium-based selective catalytic reduction (SCR) catalysts, and ammonia oxidation (AMOX) catalysts are evaluated using both nonroad transient (NRTC) and steady (8-mode NRSC) cycles in order to understand both component- and system-level effects of diesel aftertreatment on gas-phase, semivolatile, and particle-phase and particle-bound unregulated organic emissions. Organic emissions reported in this work include total hydrocarbon (THC), n-alkanes, branched alkanes, saturated cycloalkanes, aromatics, aldehydes, ketones, hopanes, steranes, and soluble organic fraction (SOF). Brake-specific emissions are reported for four configurations, including engine-out, DOC+CuZ-SCR+AMOX, V-SCR+AMOX, and DOC+DPF+CuZ-SCR+AMOX, and conversion of engine-out emissions is reported for the three aftertreatment configurations. Mechanisms responsible for the reduction of organic species are discussed in detail. This summary of emissions from a current nonroad diesel engine equipped with advanced aftertreatment can be used to more accurately model the impact of anthropogenic emissions on the atmosphere with tools such as the U.S. Environmental Protection Agency’s Motor Vehicle Emissions Simulator (MOVES2014a) model.

Implications: Anthropogenic emissions are a source of significant human health and environmental risk. This study, focused on the treatment of exhaust emissions from a modern nonroad diesel engine with a variety of aftertreatment configurations, examines the impact that human industrial activity can have on air pollution. In particular, we focus on the remediation of gas-phase and semivolatile organic emissions by emission reduction technologies. This detailed summary of emissions from a current nonroad diesel engine equipped with advanced aftertreatment can be used to more accurately model the impact of anthropogenic emissions on the atmosphere with tools such as the U.S. Environmental Protection Agency’s MOVES2014a model.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.