Forecasting air pollution potential: a synoptic climatological approach

This paper describes the development and application of an air pollution potential (APP) forecast model based on a synoptic climatological approach in a heavily industrialized area in Durban, South Africa. The aim of the forecasting procedure, based on a system of orange, red, and all-clear alerts, was to give industry advance warning of periods of poor atmospheric dispersion so that it could take action to reduce emissions. The key meteorological parameter in accurately identifying the commencement of an APP episode was found to be negative surface pressure tendency. Wind direction was the most useful parameter in estimating the end point of an APP episode. The model was very successful in identifying periods of elevated SO2, but there is a need for further refinement in forecasting the end point of an episode.     

Pulmonary Function Changes Associated with an Air Pollution Episode in January 1987

In January 1987, an air pollution episode occurred In central and western Europe. Levels of SO₂, NO₂, black smoke, sulphates and other components were elevated, with 24 hour average concentrations of SO₂ reaching a maximum of close to 300 μg/m3 In an area In the southeast of the Netherlands. Pulmonary function was measured In a group of children of 6- 12 years old at the end of the episode, and also two and three and a half weeks after the episode. A baseline lung function value was obtained about three months before the episode. Pulmonary function growth between baseline and retest dates was estimated from a simple growth model which was validated using measured pulmonary function growth data from a longitudinal study. A decline of pulmonary function (FVC, FEV₁ and PEF) from predicted baseline levels was observed, starting on the last day of the episode. Two weeks after the episode, FVC, FEV₁ PEF and MMEF were all decreased, and three and a half weeks after the episode, there was still a deficit compared to predicted baseline levels for FVC and FEV₁     

Performance and Diagnostic Evaluation of Ozone Predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study

Abstract

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O₃) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O₃ was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O₃ on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O₃ concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O₃ very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within [H11006]20%. The model reproduced 53.3% of the observed hourly O₃ within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites.The comparison of modeled and observed lidar O₃ vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64 –77% of observed NO₂ photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O₃/nitrogen oxide (NO_x) ratios suggests that the site is mainly under strongly NO_x-sensitive conditions (>53%). It was found that the modeled lower limits of the O₃ production efficiency values (inferred from O₃-CO correlation) are close to the observations.     

Modeling of Potential Power Plant Plume Impacts on Dallas-Fort Worth Visibility

ABSTRACT

During wintertime, haze episodes occur in the Dallas-Ft. Worth (DFW) urban area. Such episodes are characterized by substantial light scattering by particles and relatively low absorption, leading to so-called “white haze.” The objective of this work was to assess whether reductions in the emissions of SO₂ from specific coal-fired power plants located over 100 km from DFW could lead to a discernible change in the DFW white haze. To that end, the transport, dispersion, deposition, and chemistry of the plume of a major power plant were simulated using a reactive plume model (ROME). The realism of the plume model simulations was tested by comparing model calculations of plume concentrations with aircraft data of SF₆ tracer concentrations and ozone concentrations. A second-order closure dispersion algorithm was shown to perform better than a first-order closure algorithm and the empirical Pasquill-Gifford-Turner algorithm. For plume impact assessment, three actual scenarios were simulated, two with clear-sky conditions and one with the presence of fog prior to the haze. The largest amount of sulfate formation was obtained for the fog episode. Therefore, a hypothetical scenario was constructed using the meteorological conditions of the fog episode with input data values adjusted to be more conducive to sulfate formation. The results of the simulations suggest that reductions in the power plant emissions lead to less than proportional reductions in sulfate concentrations in DFW for the fog scenario. Calculations of the associated effects on light scattering using Mie theory suggest that reduction in total (plume + ambient) light extinction of less than 13% would be obtained with a 44% reduction in emissions of SO₂ from the modeled power plant.     

Air Quality Modeling of Interpollutant Trading for Ozone Precursors in an Urban Area

Abstract

Emission trading is a market‐based approach designed to improve the efficiency and economic viability of emission control programs; emission trading has typically been confined to trades among single pollutants. Interpollutant trading (IPT), as described in this work, allows for trades among emissions of different compounds that affect the same air quality end point, in this work, ambient ozone (O₃) concentrations. Because emissions of different compounds impact air quality end points differently, weighting factors or trading ratios (tons of emissions of nitrogen oxides (NO_x) equivalent to a ton of emissions of volatile organic compounds [VOCs]) must be developed to allow for IPT. In this work, IPT indices based on reductions in O₃ concentrations and based on reductions in population exposures to O₃ were developed and evaluated using a three‐dimensional gridded photochemical model for Austin, TX, a city currently on the cusp of nonattainment with the National Ambient Air Quality Standards for O₃ concentrations averaged over 8 hr. Emissions of VOC and NO_x from area and mobile sources in Austin are larger than emissions from point sources. The analysis indicated that mobile and area sources exhibited similar impacts. Trading ratios based on maximum O₃ concentration or population exposure were similar. In contrast, the trading ratios did exhibit significant (more than a factor of two) day‐to‐day variability. Analysis of the air quality modeling indicated that the daily variability in trading ratios could be attributed to daily variations in both emissions and meteorology.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Comment on “Measuring Air Quality Levels Inexpensively at Multiple Locations by Random Sampling”

ABSTRACT

In order to evaluate the spatial variation of aerosol (particulate matter with aerodynamic diameter ≤10 μm [PM₁₀]) and ozone (O₃) concentrations and characterize the atmospheric conditions that lead to O₃ and PM₁₀-rich episodes in southern Italy during summer 2007, an intensive sampling campaign was simultaneously performed, from middle of July to the end of August, at three ground-based sites (marine, urban, and high-altitude monitoring stations) in Calabria region. A cluster analysis, based on the prevailing air mass backward trajectories, was performed, allowing to discriminate the contribution of different air masses origin and paths. Results showed that both PM₁₀ and O₃ levels reached similar high values when air masses originated from the industrialized continental Europe as well as under the influence of wildfire emissions. Among natural sources, dust intrusion and wildfire events seem to involve a marked impact on the recorded data. Typical fair weather of Mediterranean summer and persisting anticyclone system at synoptic scale were indeed favorable conditions to the arrival of heavily dust-loaded air masses over three periods of consecutive days and more than half of the observed PM₁₀ daily exceedances have been attributed to Saharan dust events. During the identified dust outbreaks, a consistent increase in PM₁₀ levels with a concurrent decrease in O₃ values was also observed and discussed.

IMPLICATIONS In the summertime, the central-southern Mediterranean Basin is heavily affected by Saharan dust outbreaks and wildfire events. A focus on their significant influence on either oxidizing capacity of the atmosphere and air quality over Calabria, southern Italy, was here presented. Similar studies for most regions surrounding the Mediterranean Basin are needed to implement effective emission reduction measures, to prevent apparent air quality parameter exceedances and to define an appropriate health alert system. Because the frequency of these events is expected to increase due to climate change, these studies could even be a valid effort to better understand and characterize such atmospheric variations.     

Trends in Meteorologically Adjusted Ozone Concentrations in Six Kentucky Metro Areas, 1998–2002

Abstract

Meteorologically adjusted ozone (O₃) concentrations during five recent O₃ seasons (1998-2002) were computed for six Kentucky metro areas using a nonlinear regression model originally developed for forecasting ground-level O₃ concentrations. The meteorological adjustment procedure was based on modifying actual measured O₃ concentrations according to model-predicted responses to climate departures with respect to a reference year. For all six Kentucky metro areas, meteorologically adjusted O₃ concentrations declined over the five-year period. The linear best-fit rate of decline in mean adjusted O₃ concentrations ranged from 0.9 to 2.6 ppb/yr for these metro areas; the average rate of decline was 1.6 ppb/yr. The rates of decline in meteorologically adjusted extreme value (e.g., 95th percentile) concentrations were approximately the same, but there is greater statistical uncertainty concerning the extreme value trends.     

Development of a Speciated,Hourly, and Gridded Air Pollutants Emission Modeling System—A Case Study on the Precursors of Photochemical Smog in the Seoul Metropolitan Area,Korea

ABSTRACT

A speciated, hourly, and gridded air pollutants emission modeling system (SHEMS) was developed and applied in predicting hourly nitrogen dioxide (NO₂) and ozone (O₃) levels in the Seoul Metropolitan Area (SMA). The primary goal of the SHEMS was to produce a systemized emission inventory for air pollutants including ozone precursors for modeling air quality in urban areas.

The SHEMS is principally composed of three parts: (1) a pre-processor to process emission factors, activity levels, and spatial and temporal information using a geographical information system; (2) an emission model for each source type; and (3) a post-processor to produce report and input data for air quality models through database modeling. The source categories in SHEMS are point, area, mobile, natural, and other sources such as fugitive emissions. The emission database produced by SHEMS contains 22 inventoried compounds: sulfur dioxide, NO₂, carbon monoxide, and 19 speciated volatile organic compounds. To validate SHEMS, the emission data were tested with the Urban Airshed Model to predict NO₂ and O₃ concentrations in the SMA during selected episode days in 1994. The results turned out to be reliable in describing temporal variation and spatial distribution of those pollutants.     

Size mass distribution of water-soluble ionic species and gas conversion to sulfate and nitrate in particulate matter in southern Taiwan

A Micro-Orifice Uniform Deposition Impactor (MOUDI) and a Nano-MOUDI were employed to determine the size-segregated mass distributions of ambient particulate matter (PM) and water-soluble ionic species for particulate constituents. In addition, gas precursors, including HCl, HONO, HNO₃, SO₂, and NH₃ gases, were analyzed by an annular denuder system. PM size mass distribution, mass concentration, and ionic species concentration were measured during the day and at night during episode and non-episode periods in winter and summer. Average total suspended particle (TSP) concentrations during episode days in winter were as high as 153?±?33 μg/m³, and PM mass concentrations in summer were as low as one-third of that in winter. Generally, PM concentration at night was higher than that in the daytime in southern Taiwan during the sampling periods. In winter during the episode periods, the size-segregated mass distribution of PM mass concentration was mostly in the 0.32–3.2-μm range, and the PM concentration increased significantly in the range of 0.32–3.2 μm at night. Ammonium, nitrate, and sulfate were the dominant water-soluble ionic species in PM, contributing 34–48 % of TSP mass. High concentrations of ammonia (12.9–49 μg/m³) and SO₂ (2.6–27 μg/m³) were observed in the gas precursors. The conversion ratio was high in the PM size range of 0.18–3.2 μm both during the day and at night in winter, and the conversion ratio of episode days was 20 % higher than that of non-episode days. The conversion factor was high for both nitrogen and sulfur species at nighttime, especially on episode days.     

A hierarchical Bayesian model to estimate and forecast ozone through space and time

A Bayesian hierarchical regime switching model describing the spatial–temporal behavior of ozone (O₃) within a domain covering Lake Michigan during spring–summer 1999 is developed. The model incorporates linkages between ozone and meteorology. It is specifically formulated to identify meteorological regimes conducive of high ozone levels and allow ozone behavior during these periods to be different from typical ozone behavior. The model is used to estimate or forecast spatial fields of O₃ conditional on observed (or forecasted) meteorology including temperature, humidity, pressure, and wind speed and direction. The model is successful at forecasting the onset of periods of high ozone levels, but more work is needed to also accurately identify departures from these periods.     

A Protocol to Select High Quality Datasets of Ecotoxicity Values for Pesticides

Abstract

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.     

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.     

Shifted power-law relationship between NO2 concentration and the distance from a highway: A new dispersion model based on the wind profile model

In this paper, a shifted power-law model, based on the wind profile model, had been supposed to simulate concentration gradient of nitrogen dioxide (NO₂) with distance from a highway. Field experiments were performed for NO₂ gradients from a highway in Shanghai by using passive samplers. The shifted power-law model was fitted well with experimental results of field experiments both in this study and in the literature. The results not only verified the validity of shifted power-law relationship between NO₂ concentration and the distance from a highway, but also partially demonstrated that there were some significant similarities between wind profile and air pollutants concentration profile near highway. With known concentration of chosen reference point and appropriate value of the parameter k, the model could be practically applied for predicting the NO₂ distributions near a highway. The methods of determining the parameter k were also discussed for further detailed studies.     

Prediction of daily averaged PM10 concentrations by statistical time-varying model

In this study, a time-varying statistical model, TVAREX, was proposed for daily averaged PM₁₀ concentrations forecasting of coastal cities. It is a Kalman filter based autoregressive model with exogenous inputs depending on selected meteorological properties on the day of prediction. The TVAREX model was evaluated and compared to an ANN model, trained with the Levenberg–Marquardt backpropagation algorithm subjected to the same set of inputs. It was found that the error statistics of the TVAREX model in general were comparable to those of the ANN model, but the TVAREX model was more efficient in capturing the PM₁₀ pollution episodes due to its online nature, therefore having an appealing advantage for implementation.     

Resolving the interactions between population density and air pollution emissions controls in the San Joaquin Valley,USA

The effectiveness of emissions control programs designed to reduce concentrations of airborne particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) in California's San Joaquin Valley was studied in the year 2030 under three growth scenarios: low, medium, and high population density. Base-case inventories for each choice of population density were created using a coupled emissions modeling system that simultaneously considered interactions between land use and transportation, area source, and point source emissions. The ambient PM_2.5 response to each combination of population density and emissions control was evaluated using a regional chemical transport model over a 3-week winter stagnation episode. Comparisons between scenarios were based on regional average and population-weighted PM_2.5 concentrations. In the absence of any emissions control program, population-weighted concentrations of PM_2.5 in the future San Joaquin Valley are lowest under growth scenarios that emphasize low population density. A complete ban on wood burning and a 90% reduction in emissions from food cooking operations and diesel engines must occur before medium- to high-density growth scenarios result in lower population-weighted concentrations of PM_2.5. These trends partly reflect the fact that existing downtown urban cores that naturally act as anchor points for new high-density growth in the San Joaquin Valley are located close to major transportation corridors for goods movement. Adding growth buffers around transportation corridors had little impact in the current analysis, since the 8-km resolution of the chemical transport model already provided an artificial buffer around major emissions sources.

Assuming that future emissions controls will greatly reduce or eliminate emissions from residential wood burning, food cooking, and diesel engines, the 2030 growth scenario using “as-planned” (medium) population density achieves the lowest population-weighted average PM_2.5 concentration in the future San Joaquin Valley during a severe winter stagnation event.

Implications: The San Joaquin Valley is one of the most heavily polluted air basins in the United States that are projected to experience strong population growth in the coming decades. The best plan to improve air quality in the region combines medium- or high-density population growth with rigorous emissions controls. In the absences of controls, high-density growth leads to increased population exposure to PM_2.5 compared with low-density growth scenarios (urban sprawl).     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

Using a process-based model to understand dynamics of Chinese agricultural and water technology development from 8000 bc to 1911 ad

Advancements in technology are inextricably bound to our society and the natural environment. However, how the development process of a technology system interacts with both remains unclear. We propose a process model to understand the complex dynamics among technology, society, and the environment via seven interactive elements: technologies, actors, receiving bodies, natural contexts, social contexts, temporal–spatial contexts, and outcomes. The model was applied to agricultural and water technology development in China from 8000 bc to 1911 ad. Our findings show that these elements did not play equally important roles in different periods of the development in ancient China, with social contexts most dominating during the earlier periods and both social and environmental concerns arising towards the later periods. The proposed model, by identifying the elements in the technology development that should be strengthened, can act as an analysis device to assist in reconfiguring a more sustainable socio-technological system.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01424-7) contains supplementary material, which is available to authorized users.     

Identification of sources of atmospheric PM at the Pittsburgh Supersite—Part III: Source characterization

Single-particle mass spectrometry data collected during the Pittsburgh Supersite experiment was used to isolate an episode on 27 October 2001 when the measurement site was primarily influenced by emissions from coal combustion sources. Results showed that (a) 60–80% of the particles detected during this event belonged to the Na/Si/K/Ca/Fe/Ga/Pb particle class associated with coal combustion emissions, (b) observation of this class was an isolated event occurring only during the hours of 06:00–14:00 EST, and (c) the detection of these particles was highly correlated with shifts in wind direction. Coincident SMPS, TEOM PM_2.5, SO₂, NO_x, and O₃ measurements were in excellent agreement with the single-particle results in terms of both identifying and characterizing this event. The three most likely point sources of these particles were isolated and Gaussian plume dispersion models were used in reverse to predict their particle number, particle mass, and gas phase emissions. Calculated mass emission rates were in general agreement with the US EPA National Emissions Inventory (NEI) database emissions estimates and the Title V PM₁₀ limit. The largest of the three sources emits about 2.4×10¹⁷ fine and ultrafine particles per second.     

Emission Reductions Needed to Meet the Standard for Ozone in Southern California: Effect of Boundary Conditions

Abstract

Combinations of total reactive organic gas (ROG) and nitrogen oxide (NO_x) emissions that do not exceed the National Ambient Air Quality Standard (NAAQS) for ozone for the meteorological conditions of the August 26-28, 1987 SCAQS episode, have been determined using the California Institute of Technology (CIT) photochemical air quality model. The sensitivity of these combinations to pollutant boundary conditions is examined.