Dispersion modelling: a tool for local air quality management

A local air quality management system, and the use of dispersion modelling as a tool for the system, are described. The Turkish town of Gehze is used as a model.     

Development of risk-based air quality management strategies under impacts of climate change

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies. Implications: Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.     

Effect of climate change on air quality

Air quality is strongly dependent on weather and is therefore sensitive to climate change. Recent studies have provided estimates of this climate effect through correlations of air quality with meteorological variables, perturbation analyses in chemical transport models (CTMs), and CTM simulations driven by general circulation model (GCM) simulations of 21st-century climate change. We review these different approaches and their results. The future climate is expected to be more stagnant, due to a weaker global circulation and a decreasing frequency of mid-latitude cyclones. The observed correlation between surface ozone and temperature in polluted regions points to a detrimental effect of warming. Coupled GCM–CTM studies find that climate change alone will increase summertime surface ozone in polluted regions by 1–10 ppb over the coming decades, with the largest effects in urban areas and during pollution episodes. This climate penalty means that stronger emission controls will be needed to meet a given air quality standard. Higher water vapor in the future climate is expected to decrease the ozone background, so that pollution and background ozone have opposite sensitivities to climate change. The effect of climate change on particulate matter (PM) is more complicated and uncertain than for ozone. Precipitation frequency and mixing depth are important driving factors but projections for these variables are often unreliable. GCM–CTM studies find that climate change will affect PM concentrations in polluted environments by ±0.1–1 μg m^?3 over the coming decades. Wildfires fueled by climate change could become an increasingly important PM source. Major issues that should be addressed in future research include the ability of GCMs to simulate regional air pollution meteorology and its sensitivity to climate change, the response of natural emissions to climate change, and the atmospheric chemistry of isoprene. Research needs to be undertaken on the effect of climate change on mercury, particularly in view of the potential for a large increase in mercury soil emissions driven by increased respiration in boreal ecosystems.     

Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.     

Development of a low-cost air sensor package and indoor air quality monitoring in a California middle school: Detection of a distant wildfire

A low-cost air sensor package was used to monitor indoor air quality (IAQ) in a classroom at the Albany Middle School in the San Francisco Bay Area of California. A rapid increase in carbon dioxide (CO₂) was observed in the classroom as soon as it is occupied. When the classroom is unoccupied, the CO₂ levels decay slowly toward the outdoor background level. All high CO₂ concentrations observed inside the classroom, above the outdoor background, was due to exhaling of the occupants. The CO₂ concentrations generally exceed the recommended level of 1000 ppb towards the end of the school day. The exceedances and slow decay may suggest that the ventilation rate in this school is not sufficient. The particulate level in the classroom was low until a distant wildfire advected large amount of particulate matter to the San Francisco Bay Area. Very high (10–15 times compared to the background) particle numbers (per m³ of particles with diameter >0.3 µm) were observed in the classroom during the wildfire. These particles were relatively small (0.3–1.0 µm) and the filters (MERV 8) of the ventilation system were unable to filter them out. Therefore, the measurements made by low-cost particle counters can inform the school administrators of adverse IAQ during future wildfire (or other combustion) events. The particle number was independent of the occupation before and during the wildfire suggesting that all observed particles were infiltrated into the classroom from outside. Consistent with previous studies, no appreciable increase in the local ambient CO₂ background was observed during this distant wildfire event.

Implications: Low-cost air sensors are effective in monitoring indoor air quality in classrooms. The CO₂ levels in classrooms are mainly generated indoors due to exhalation of occupants. Concentration of CO₂ generally exceed the recommended level of 1000 ppb towards the end of the school day. In contrast, the particulate matter mostly comes from outdoors and small particles penetrate though the filters normally used at schools. Distant wildfires do not increase the local CO₂ background appreciably, but significantly increase the particulate matter concentrations both indoors and outdoors. Further investigations are needed to assure that ventilation rates in classrooms are sufficiently health protective.     

New indicator approaches for effective urban air quality management

Measurements of urban air quality at monitoring stations in developed countries have frequently involved the criteria gaseous pollutants, particulates, hazardous air pollutants, perceived air quality and relevant meteorological conditions. Large numbers of indicators have therefore been established to quantify emissions, concentrations and environmental and human health impacts of each of these groups of substances. To simplify the data for management, several indicators have been grouped together to form urban air quality indices but the weightings of individual variables is contentious. In industrialising and developing countries, data may be limited and traditional air pollutant indicators cannot often be constructed. The emphasis therefore has to be placed on the development of policy-relevant indicators, such as Response Indicators that reflect different policy principles for regulating air pollutant emissions. Indices that quantify the air quality management capabilities and capacities at the city level provide further useful decision-relevant tools. Four sets of indices, namely, 1. air quality measurement capacity, 2. data assessment and availability, 3. emissions estimates, and 4. management enabling capabilities, and a composite index to evaluate air quality management capability, were constructed and applied to 80 cities. The indices revealed that management capability varied widely between the cities. In some of the cities, existing national knowledge on urban air quality could have been more effectively used for management. It was concluded that for effective urban air quality management, a greater emphasis should be given, not just to monitoring and data capture programmes, but to the development of indicators and indices that empower decision-makers to initiate management response strategies. Over-reliance on restricted, predetermined sets of traditional air quality indicators should be avoided.     

Characterization of urban air quality using GIS as a management system

Keeping the air quality acceptable has become an important task for decision makers as well as for non-governmental organizations. Particulate and gaseous emissions of pollutant from industries and auto-exhausts are responsible for rising discomfort, increasing airway diseases, decreasing productivity and the deterioration of artistic and cultural patrimony in urban centers. A model to determine the air quality in urban areas using a geographical information system will be presented here. This system permits the integration, handling, analysis and simulation of spatial and temporal data of the ambient concentration of the main pollutant. It allows the users to characterize and recognize areas with a potential increase or improvement in its air pollution situation. It is also possible to compute past or present conditions by changing basic input information as traffic flow, or stack emission rates. Additionally the model may be used to test the compliance of local standard air quality, to study the environmental impact of new industries or to determine the changes in the conditions when the vehicle circulation is increased.     

Retrospective assessment of air quality management practices in Taiwan

In 1995, Taiwan's Environmental Protection Administration (EPA/TW) instituted a policy of levying emission taxes on polluters in order to combat the rampant national issue of pollution. Since that time, pollution control strategies, tightening exhaust emission standards for industry, improvements in fuel quality, and new stricter vehicle emission standards, etc., have been implemented. This study evaluates the effectiveness of these measures and examines the improvement of Taiwan's air quality. In this paper, we conduct a detailed analysis of change in the concentrations of pollutants (SO₂, NO_x and particulate matter [PM]) between two three-year periods (from 1996 to1998 and from 2000 to 2002). The pollution levels were generally lower in the latter period. Concentrations at 14 EPA/TW stations in central Taiwan were simulated and source apportionment analyses in three of Central Taiwan's largest cities were conducted using a trajectory transfer-coefficient air quality model. Correlation coefficients (r) between simulations and observations for the monthly means of the concentrations of SO₂, NO_x, PM_2.5 and PM₁₀ during the study periods at the 14 stations are 0.56, 0.63, 0.70 and 0.31, respectively. The sulfur control policy greatly reduced SO₂ concentration island-wide, a stringent emission standard put into place for gasoline vehicles reduced NO_x concentration along highways, and an emissions tax placed on construction sites, as well as a regular program for road-dust sweeping, reduced primary particulate matter. Among all of the pollution abatement policies implemented, the most effective method for reducing PM_2.5 concentrations in the three largest cities involved the reduction of fine ammonium sulfate aerosols from point sources (56–63% of net PM_2.5 reduction). The next largest reduction was attributed to a diminishment in primary PM_2.5 emanating from point sources (27–56% of net PM_2.5 reduction). Secondary particulate matter, especially sulfate, was reduced from distances up to 150 km leeward of major pollution point sources such as Taichung Power Plant.     

Himalayan alpine ecohydrology: An urgent scientific concern in a changing climate

Climate change is projected to have important impacts on snow and vegetation distribution in global mountains. Despite this, the coupling of ecological shifts and hydrological processes within alpine zones has not attracted significant scientific attention. As the largest and one of the most climatically sensitive mountain systems, we argue that Himalayan alpine ecohydrological processes require urgent scientific attention because up to 1.6 billion people rely on water supplies from the mountains. We review studies from global mountain systems to highlight the importance of considering ecohydrological impacts within Himalayan alpine zones (4100–6000 m.a.s.l), explaining mechanisms for interactions between snow and dwarf plants. Our findings highlight the paucity of monitoring stations within Himalayan alpine systems. We suggest that it is likely that alpine ecological shifts will impact hydrological processes, but we found that specific mechanisms and functional relationships are missing for Himalayan systems, so the strength and direction of ecohydrological relationships is currently unknown. We advocate for more purposeful and widespread monitoring efforts below glaciers and above the treeline, calling for new experiments to query the role of small plants within the Himalayan alpine hydrological system. We outline the need for community engagement with alpine ecohydrological experiments, and we explain how new snow and vegetation products derived from remote sensing observations have the potential to improve scientific understanding of the interacting effects of warming and ecohydrological factors in this sensitive region.     

The development of effects-based air quality management regimes

This paper considers the evolution of attempts to control and manage air pollution, principally but not exclusively focussing upon the challenge of managing air pollution in urban environments. The development and implementation of a range of air pollution control measures are considered. Initially the measures implemented primarily addressed point sources, a small number of fuel types and a limited number of pollutants. The adequacy of such a source-control approach is assessed within the context of a changing and challenging air pollution climate. An assessment of air quality management in the United Kingdom over a 50-year timeframe exemplifies the range of issues and challenges in contemporary air quality management. The need for new approaches is explored and the development and implementation of an effects-based, risk management system for air quality regulation is evaluated.     

Development of a GIS-based decision support system for urban air quality management in the city of Istanbul

A decision support system has been developed for urban air quality management in the metropolitan area of Istanbul. The system is based on CALMET/CALPUFF dispersion modeling system, digital maps, and related databases to estimate the emissions and spatial distribution of air pollutants with the help of a GIS software. The system estimates ambient air pollution levels at high temporal and spatial resolutions and enables mapping of emissions and air quality levels. Mapping and scenario results can be compared with air quality limits. Impact assessment of air pollution abatement measures can also be carried out.     

Sequential box models for indoor air quality: Application to airliner cabin air quality

In this paper we present the development and application of a model for indoor air quality. The model represents a departure from the standard box models typically used for indoor environments which has applicability in residences and office buildings. The model has been developed for a physical system consisting of sequential compartments which communicate only with adjacent compartments. Each compartment may contain various source and sink terms for a pollutant as well as leakage, and air transfer from adjacent compartments. The mathematical derivation affords rapid calculation of equilibrium concentrations in an essentially unlimited number of compartments. The model has been applied to air quality in the passenger cabin of three commercial aircraft. Simulations have been performed for environmental tobacco smoke (ETS) under two scenarios, CO₂ and water vapor. Additionally, concentrations in one aircraft have been simulated under conditions different from the standard configuration. Results of the simulations suggest the potential for elevated concentrations of ETS in smoking sections of non-air-recirculating aircraft and throughout the aircraft when air is recirculated. Concentrations of CO₂ and water vapor are consistent with expected results. We conclude that this model may be a useful tool in understanding indoor air quality in general and on aircraft in particular.     

Particulate air quality model predictions using prognostic vs. diagnostic meteorology in central California

Comparisons were made between three sets of meteorological fields used to support air quality predictions for the California Regional Particulate Air Quality Study (CRPAQS) winter episode from December 15, 2000 to January 6, 2001. The first set of fields was interpolated from observations using an objective analysis method. The second set of fields was generated using the WRF prognostic model without data assimilation. The third set of fields was generated using the WRF prognostic model with the four-dimensional data assimilation (FDDA) technique. The UCD/CIT air quality model was applied with each set of meteorological fields to predict the concentrations of airborne particulate matter and gaseous species in central California. The results show that the WRF model without data assimilation over-predicts surface wind speed by ～30% on average and consequently yields under-predictions for all PM and gaseous species except sulfate (S(VI)) and ozone(O₃). The WRF model with FDDA improves the agreement between predicted and observed wind and temperature values and consequently yields improved predictions for all PM and gaseous species. Overall, diagnostic meteorological fields produced more accurate air quality predictions than either version of the WRF prognostic fields during this episode. Population-weighted average PM_2.5 exposure is 40% higher using diagnostic meteorological fields compared to prognostic meteorological fields created without data assimilation. These results suggest diagnostic meteorological fields based on a dense measurement network are the preferred choice for air quality model studies during stagnant periods in locations with complex topography.     

Assessment of the emissions and air quality impacts of biomass and biogas use in California

It is estimated that there is sufficient in-state “technically” recoverable biomass to support nearly 4000 MW of bioelectricity generation capacity. This study assesses the emissions of greenhouse gases and air pollutants and resulting air quality impacts of new and existing bioenergy capacity throughout the state of California, focusing on feedstocks and advanced technologies utilizing biomass resources predominant in each region. The options for bioresources include the production of bioelectricity and renewable natural gas (NG). Emissions of criteria pollutants and greenhouse gases are quantified for a set of scenarios that span the emission factors for power generation and the use of renewable natural gas for vehicle fueling. Emissions are input to the Community Multiscale Air Quality (CMAQ) model to predict regional and statewide temporal air quality impacts from the biopower scenarios. With current technology and at the emission levels of current installations, maximum bioelectricity production could increase nitrogen oxide (NO_x) emissions by 10% in 2020, which would cause increases in ozone and particulate matter concentrations in large areas of California. Technology upgrades would achieve the lowest criteria pollutant emissions. Conversion of biomass to compressed NG (CNG) for vehicles would achieve comparable emission reductions of criteria pollutants and minimize emissions of greenhouse gases (GHG). Air quality modeling of biomass scenarios suggest that applying technological changes and emission controls would minimize the air quality impacts of bioelectricity generation. And a shift from bioelectricity production to CNG production for vehicles would reduce air quality impacts further. From a co-benefits standpoint, CNG production for vehicles appears to provide the best benefits in terms of GHG emissions and air quality.

Implications:?This investigation provides a consistent analysis of air quality impacts and greenhouse gas emissions for scenarios examining increased biomass use. Further work involving economic assessment, seasonal or annual emissions and air quality modeling, and potential exposure analysis would help inform policy makers and industry with respect to further development and direction of biomass policy and bioenergy technology alternatives needed to meet energy and environmental goals in California.     

Phosphorus management in Europe in a changing world

Food production in Europe is dependent on imported phosphorus (P) fertilizers, but P use is inefficient and losses to the environment high. Here, we discuss possible solutions by changes in P management. We argue that not only the use of P fertilizers and P additives in feed could be reduced by fine-tuning fertilization and feeding to actual nutrient requirements, but also P from waste has to be completely recovered and recycled in order to close the P balance of Europe regionally and become less dependent on the availability of P-rock reserves. Finally, climate-smart P management measures are needed, to reduce the expected deterioration of surface water quality resulting from climate-change-induced P loss.     

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial,California, USA

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R² for converted hourly averaged Dylos mass measurements versus a PM_2.5 BAM was 0.79 and that versus a PM₁₀ BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM_2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R² = 0.35–0.81).

Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.