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Exposure to bioaerosol allergens such as pollen can cause exacerbations of allergenic airway disease (AAD) in sensitive populations, and thus cause serious public health problems. Assessing these health impacts by linking the airborne pollen levels, concentrations of respirable allergenic material, and human allergenic response under current and future climate conditions is a key step toward developing preventive and adaptive actions. To that end, a regional-scale pollen emission and transport modeling framework was developed that treats allergenic pollens as non-reactive tracers within the WRF/CMAQ air-quality modeling system. The Simulator of the Timing and Magnitude of Pollen Season (STaMPS) model was used to generate a daily pollen pool that can then be emitted into the atmosphere by wind. The STaMPS is driven by species-specific meteorological (temperature and/or precipitation) threshold conditions and is designed to be flexible with respect to its representation vegetation species and plant functional types (PFTs). The hourly pollen emission flux was parameterized by considering the pollen pool, friction velocity, and wind threshold values. The dry deposition velocity of each species of pollen was estimated based on pollen grain size and density. An evaluation of the pollen modeling framework was conducted for southern California for the period from March to June 2010. This period coincided with observations by the University of Southern California's Children's Health Study (CHS), which included O3, PM2.5, and pollen count, as well as measurements of exhaled nitric oxide in study participants. Two nesting domains with horizontal resolutions of 12 km and 4 km were constructed, and six representative allergenic pollen genera were included: birch tree, walnut tree, mulberry tree, olive tree, oak tree, and brome grasses. Under the current parameterization scheme, the modeling framework tends to underestimate walnut and peak oak pollen concentrations, and tends to overestimate grass pollen concentrations. The model shows reasonable agreement with observed birch, olive, and mulberry tree pollen concentrations. Sensitivity studies suggest that the estimation of the pollen pool is a major source of uncertainty for simulated pollen concentrations. Achieving agreement between emission modeling and observed pattern of pollen releases is the key for successful pollen concentration simulations. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years'' (2000--06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality--related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047--53) and present (2000--06) simulation results driven by PCM data and is presented in a separate paper. [ABSTRACT FROM AUTHOR]

This work presents new climate and emissions scenarios to investigate changes on future meteorology and air quality in the U.S. Here, we employ a dynamically downscaled Weather Research and Forecasting model coupled with chemistry (WRF/Chem) simulations that use two Intergovernmental Panel on Climate Change scenarios (i.e., A1B and B2) integrated with explicitly projected emissions from a novel Technology Driver Model (TDM). The projected 2046–2055 emissions show widespread reductions in most gas and aerosol species under both TDM/A1B and TDM/B2 scenarios over the U.S. The WRF/Chem simulations show that under the combined effects of the TDM/A1B climate and emission changes, the maximum daily average 8-h ozone (MDA8 h O3) increases by ~3 ppb across the U.S. mainly due to widespread increases in near-surface temperature and background methane concentrations, with some contributions from localized TDM emission changes near urban centers. For the TDM/B2 climate and emission changes, however, the MDA8 h O3 is widely decreased, except near urban centers where the relative TDM emission changes and O3 formation regimes leads to increased O3. The number of O3 exceedance days (i.e., MDA8 h O3 > 70 ppb) for the entire domain is significantly reduced by a grid cell maximum of up to 43 days (domain average ~0.5 days) and 62 days (domain average ~2 days) for the TDM/A1B and TDM/B2 scenarios, respectively, while in the western U.S., larger O3 increases lead to increases in nonattainment areas, especially for the TDM/A1B scenario. The combined effects of climate and emissions (for both A1B and B2 scenarios) will lead to widespread decreases in the daily 24-h average (DA24 h) PM2.5 concentrations, especially in the eastern U.S. (max decrease up to 93 µg m−3). The PM2.5 changes are dominated by decreases in anthropogenic emissions for both the TDM/A1B and TDM/B2 scenarios, with secondary effects on decreasing PM2.5 from climate change. The number of PM2.5 exceedance days (i.e., DA24 h PM2.5 > 35 µg m−3) is significantly reduced over the eastern U.S. under both TDM/A1B and B2 scenarios, which suggests that both climate and emission changes may synergistically lead to decreases in PM2.5 nonattainment areas in the future. [ABSTRACT FROM AUTHOR]

Abstract: Ambient air-quality data contain information about air-pollution sources that is currently under-exploited. This information could be used to assess trends in the emissions performance of specific sources, and to check at an early stage if policies or controls to reduce air-quality impacts from particular sources are working. Previous techniques for extracting such information have tended to adopt complex analyses and to rely on data from monitoring networks with many sites, thus limiting their applicability to non-specialist users and to networks with few sites. This paper describes simple techniques for ‘conditionally’ selecting data from one or two monitors, and for analysing and interpreting concentrations in terms of source performance or policy progress. Our techniques minimise the effects of variations in meteorology and source activity, so that the selected data give a more consistent indication of individual source performance. We demonstrate our techniques with a case study, in which we track the source performance of road traffic on the M4 motorway in London and show how impacts per vehicle have changed over time under different conditions of traffic flow and fleet composition. [Copyright &y& Elsevier]

This paper represents, for the first time, scientific data as a part of monitoring air quality in the city of Split. The city has a problem with the air pollution mostly caused by anthropogenic activities and partly by natural origin activities. This study presents results of monthly and seasonal variations of NO2, SO2 and black-smoke as well as the influence of meteorological parameters on observed concentration levels. Average seasonal values for SO2 ranged from 22.54 to 54.81 gg m-3, for NO2 from 48.24 to 56.38 gg m-3 and for black-smoke from 4.15 to 5.64/µg m-3. The results obtained for SO2 and black-smoke were below, while for NO2 were above the limit value of pollutants in the air recommended by Croatian Government (Air Protection Act (OG 178/04); Regulation on limit values of pollutants in air (OG 133/05)). As the result of stable meteorological conditions (high temperatures and drought), the increase in SO2 concentration was significant in summer period, while the concentrations of NO2 and black-smoke were not significantly changed during year and did not depend on other parameters. [ABSTRACT FROM AUTHOR]

Abstract: Recent acknowledgement of the sensitivity of air quality to changes in climate has initiated a closer examination of the relationships between meteorology and air quality. This paper presents the estimated response of daily air pollutant concentrations in Melbourne, Australia to local-scale meteorology. The meteorological-pollutant relationships have been assessed after controlling for long-term trends, seasonality, weekly emissions, spatial variation, and temporal persistence using the framework of generalized additive models (GAMs). Overall, results found that the aggregate impact of meteorological variables in the models explained 26.3% of the variance in O3, 21.1% in PM10, and 26.7% in NO2. This indicates that meteorology – at the local-scale, is a relatively strong driver of air quality in Melbourne. Analysis of partial residuals plots found that changes in temperature, particularly when above 35 °C, resulted in the strongest positive response for O3 (150%), PM10 (150%) and NO2 (120%). Other variables (boundary layer height, winds, water vapor pressure, radiation, precipitation, and mean sea-level pressure) displayed some importance for one or more of the pollutants, but their impact in the models was less pronounced. In sum, we provide results that form a solid foundation for understanding the importance of local-scale meteorology as a driver of regional air pollution. Additionally, these results can be used to provide a window into how changes in climate may affect air quality. [Copyright &y& Elsevier]

Abstract: In order to make projections for future air-quality levels, a robust methodology is needed that succeeds in reconstructing present-day air-quality levels. At present, climate projections for meteorological variables are available from Atmospheric-Ocean Coupled Global Climate Models (AOGCMs) but the temporal and spatial resolution is insufficient for air-quality assessment. Therefore, a variety of methods are tested in this paper in their ability to hindcast maximum 8 hourly levels of O3 and daily mean PM10 from observed meteorological data. The methods are based on a multiple linear regression technique combined with the automated Lamb weather classification. Moreover, we studied whether the above-mentioned multiple regression analysis still holds when driven by operational ECMWF (European Center for Medium-Range Weather Forecast) meteorological data. The main results show that a weather type classification prior to the regression analysis is superior to a simple linear regression approach. In contrast to PM10 downscaling, seasonal characteristics should be taken into account during the downscaling of O3 time series. Apart from a lower explained variance due to intrinsic limitations of the regression approach itself, a lower variability of the meteorological predictors (resolution effect) and model deficiencies, this synoptic-regression-based tool is generally able to reproduce the relevant statistical properties of the observed O3 distributions important in terms of European air quality Directives and air quality mitigation strategies. For PM10, the situation is different as the approach using only meteorology data was found to be insufficient to explain the observed PM10 variability using the meteorological variables considered in this study. [Copyright &y& Elsevier]

The article presents research into the application of regional dynamical downscaling for the evaluation of air quality. Meteorologists' ability to answer questions regarding the impact of climate change on air quality and vice versa depends on the ability of climatology models to simulate meteorological conditions. One atmospheric simulation is driven by a global circulation model (GCM) control simulation while a second atmospheric simulation is driven by a global reanalysis. A comparison is made of selected meteorological variables, namely diurnal wind patterns, unvented hours, precipitation, and 500-hPa heights.

The potential impacts of climate change on regional ozone (O3) and fine particulate (PM2.5) air quality in the United States (US) are investigated by linking global climate simulations with regional-scale meteorological and chemical transport models. Regional climate at 2000 and at 2030 under three Representative Concentration Pathways (RCPs) is simulated by using theWeather Research and Forecasting (WRF) model to downscale 11-year time slices from the Community Earth System Model (CESM). The downscaled meteorology is then used with the Community Multiscale Air Quality (CMAQ) model to simulate air quality during each of these 11-year periods. The analysis isolates the future air quality differences arising from climate-driven changes in meteorological parameters and specific natural emissions sources that are strongly influenced by meteorology. Other factors that will affect future air quality, such as anthropogenic air pollutant emissions and chemical boundary conditions, are unchanged across the simulations. The regional climate fields represent historical daily maximum and daily minimum temperatures well, with mean biases of less than 2K for most regions of the US and most seasons of the year and good representation of variability. Precipitation in the central and eastern US is well simulated for the historical period, with seasonal and annual biases generally less than 25 %, with positive biases exceeding 25% in the western US throughout the year and in part of the eastern US during summer. Maximum daily 8 h ozone (MDA8 O3) is projected to increase during summer and autumn in the central and eastern US. The increase in summer mean MDA8 O3 is largest under RCP8.5, exceeding 4 ppb in some locations, with smaller seasonal mean increases of up to 2 ppb simulated during autumn and changes during spring generally less than 1 ppb. Increases are magnified at the upper end of the O3 distribution, particularly where projected increases in temperature are greater. Annual average PM2.5 concentration changes range from -1.0 to 1.0 µgm-3. Organic PM2.5 concentrations increase during summer and autumn due to increased biogenic emissions. Aerosol nitrate decreases during winter, accompanied by lesser decreases in ammonium and sulfate, due to warmer temperatures causing increased partitioning to the gas phase. Among meteorological factors examined to account for modeled changes in pollution, temperature and isoprene emissions are found to have the largest changes and the greatest impact on O3 concentrations. [ABSTRACT FROM AUTHOR]

The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Groundlevel ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through airquality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000-2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone-relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3-T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where airquality models tend to overestimate the magnitude of the observed climate penalty. [ABSTRACT FROM AUTHOR]

Background: Climate change negatively impacts human health through heat stress and exposure to worsened air pollution, amongst other pathways. Indoor use of air conditioning can be an effective strategy to reduce heat exposure. However, increased air conditioning use increases emissions of air pollutants from power plants, in turn worsening air quality and human health impacts. We used an interdisciplinary linked model system to quantify the impacts of heat-driven adaptation through building cooling demand on air-quality-related health outcomes in a representative mid-century climate scenario.Methods and Findings: We used a modeling system that included downscaling historical and future climate data with the Weather Research and Forecasting (WRF) model, simulating building electricity demand using the Regional Building Energy Simulation System (RBESS), simulating power sector production and emissions using MyPower, simulating ambient air quality using the Community Multiscale Air Quality (CMAQ) model, and calculating the incidence of adverse health outcomes using the Environmental Benefits Mapping and Analysis Program (BenMAP). We performed simulations for a representative present-day climate scenario and 2 representative mid-century climate scenarios, with and without exacerbated power sector emissions from adaptation in building energy use. We find that by mid-century, climate change alone can increase fine particulate matter (PM2.5) concentrations by 58.6% (2.50 μg/m3) and ozone (O3) by 14.9% (8.06 parts per billion by volume [ppbv]) for the month of July. A larger change is found when comparing the present day to the combined impact of climate change and increased building energy use, where PM2.5 increases 61.1% (2.60 μg/m3) and O3 increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM2.5 and 6.7% of the total increase in O3 is attributable to adaptive behavior (extra air conditioning use). Health impacts assessment finds that for a mid-century climate change scenario (with adaptation), annual PM2.5-related adult mortality increases by 13,547 deaths (14 concentration-response functions with mean incidence range of 1,320 to 26,481, approximately US$126 billion cost) and annual O3-related adult mortality increases by 3,514 deaths (3 functions with mean incidence range of 2,175 to 4,920, approximately US$32.5 billion cost), calculated as a 3-month summer estimate based on July modeling. Air conditioning adaptation accounts for 654 (range of 87 to 1,245) of the PM2.5-related deaths (approximately US$6 billion cost, a 4.8% increase above climate change impacts alone) and 315 (range of 198 to 438) of the O3-related deaths (approximately US$3 billion cost, an 8.7% increase above climate change impacts alone). Limitations of this study include modeling only a single month, based on 1 model-year of future climate simulations. As a result, we do not project the future, but rather describe the potential damages from interactions arising between climate, energy use, and air quality.Conclusions: This study examines the contribution of future air-pollution-related health damages that are caused by the power sector through heat-driven air conditioning adaptation in buildings. Results show that without intervention, approximately 5%-9% of exacerbated air-pollution-related mortality will be due to increases in power sector emissions from heat-driven building electricity demand. This analysis highlights the need for cleaner energy sources, energy efficiency, and energy conservation to meet our growing dependence on building cooling systems and simultaneously mitigate climate change. [ABSTRACT FROM AUTHOR]

Scientific research findings that span large concurrent field campaigns during the summer of 2013 in the southeast United States are synthesized and discussed. [ABSTRACT FROM AUTHOR]

Recent intensive sampling and numerical modeling studies have given insights to the processes driving atmospheric chemistry in the mid-Atlantic region. We summarize the meteorology and air quality measurements conducted as part of the NASA Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign and other mid-Atlantic sampling intensives during July 2011. We review the important findings of the work contained within the Processes Impacting NEar-Surface Atmospheric Pollutants (PINESAP) special issue and highlight the importance of intensive sampling campaigns as a means of gaining understanding of atmospheric chemistry in a changing climate. [ABSTRACT FROM AUTHOR]

The composition and relative abundance of airborne pollen in urban areas of Australia and New Zealand are strongly influenced by geographical location, climate and land use. There is mounting evidence that the diversity and quality of airborne pollen is substantially modified by climate change and land-use yet there are insufficient data to project the future nature of these changes. Our study highlights the need for long-term aerobiological monitoring in Australian and New Zealand urban areas in a systematic, standardised, and sustained way, and provides a framework for targeting the most clinically significant taxa in terms of abundance, allergenic effects and public health burden. [ABSTRACT FROM AUTHOR]

Based on a 10-yr simulation with the global air quality modeling system GEM-AQ/EC, the northern hemispheric aerosol transport with the inter-annual and seasonal variability as well as the mean climate was investigated. The intercontinental aerosol transport is predominant in the zonal direction from west to east with the ranges of interannual variability between 14% and 63%, is 0.5-2 orders of magnitude weaker in the meridional direction but with larger inter-annual variability. The aerosol transport is found to fluctuate seasonally with a factor of 5-8 between the maximum in late winter and spring and the minimum in late summer and fall. Three meteorological factors controlling the intercontinental aerosol transport and its interannual variations are identified from the modeling results: (1) Anomalies in the mid-latitude westerlies in the troposphere. (2) Variations of precipitation over the intercontinental transport pathways and (3) Changes of meteorological conditions within the boundary layer. Changed only by the meteorology, the aerosol column loadings in the free troposphere over the source regions of Europe, North America, South and East Asia vary inter-annually with the highest magnitudes of 30-37% in January and December and the lowest magnitudes of 16-20% in August and September, the interannual aerosol variability within the boundary layer influencing the surface concentrations with the magnitudes from 6% to 20% is more region-dependent. As the strongest climatic signal, the El Niño-Southern Oscillation (ENSO) can lead the anomalies in the intercontinental aerosols in El Niño- and La Niña-years respectively with the strong and weak transport of the mid-latitude westerlies and the low latitude easterlies in the Northern Hemisphere (NH). [ABSTRACT FROM AUTHOR]

We applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM2.5 to climate change. We used 2004-2008 PM2.5 observations from ~1000 sites (~200 sites for PM2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM2.5 variability, and show that 20-40% of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996-2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos) when diagnosing the effect of climate change on PM2.5, and suggest that analysis of meteorological modes of variability provides a computationally more affordable approach for this purpose than coupled GCM-CTM studies. [ABSTRACT FROM AUTHOR]

In recent years there has been considerable advancement in our scientific understanding of the linkages and interactions between climate change and air quality. A warmer, evolving climate is likely to have severe consequences for air quality due to impacts on pollution sources and meteorology. Climate-induced changes to sources of tropospheric ozone precursor gases and to atmospheric circulation are likely to lead to changes in both the concentration and dispersion of near-surface ozone that could act to offset improvements in air quality. The control of air pollutants through air quality management is also likely to impact on climate change, with reductions in ozone, particulate matter and sulphur dioxide being of particular interest. The improved understanding of the relationship between air quality and climate change provides a scientific basis for policy interventions. After a review of the scientific linkages, the potential to include climate change considerations in air quality management planning processes in South Africa was examined. [ABSTRACT FROM AUTHOR]

A high-resolution global aerosol model (Oslo CTM2) driven by meteorological data and allowing a comparison with a variety of aerosol observations is used to simulate radiative forcing (RF) of the direct aerosol effect. The model simulates all main aerosol components, including several secondary components such as nitrate and secondary organic carbon. The model reproduces the main chemical composition and size features observed during large aerosol campaigns. Although the chemical composition compares best with ground-based measurement over land for modelled sulphate, no systematic differences are found for other compounds. The modelled aerosol optical depth (AOD) is compared to remote sensed data from AERONET ground and MODIS and MISR satellite retrievals. To gain confidence in the aerosol modelling, we have tested its ability to reproduce daily variability in the aerosol content, and this is performing well in many regions; however, we also identified some locations where model improvements are needed. The annual mean regional pattern of AOD from the aerosol model is broadly similar to the AERONET and the satellite retrievals (mostly within 10-20%). We notice a significant improvement from MODIS Collection 4 to Collection 5 compared to AERONET data. Satellite derived estimates of aerosol radiative effect over ocean for clear sky conditions differs significantly on regional scales (almost up to a fac- tor two), but also in the global mean. The Oslo CTM2 has an aerosol radiative effect close to the mean of the satellite derived estimates. We derive a radiative forcing (RF) of the direct aerosol effect of -0.35 Wm-2 in our base case. Implementation of a simple approach to consider internal black carbon (BC) mixture results in a total RF of -0.28 Wm-2. Our results highlight the importance of carbonaceous particles, producing stronger individual RF than considered in the recent IPCC estimate; however, net RF is less different. A significant RF from secondary organic aerosols (SOA) is estimated (close to -0.1 Wm-2). The SOA also contributes to a strong domination of secondary aerosol species for the aerosol composition over land. A combination of sensitivity simulations and model evaluation show that the RF is rather robust and unlikely to be much stronger than in our best estimate. [ABSTRACT FROM AUTHOR]