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Poor air quality is recognised as the most important environmental health issue of our time. Meteorological variables like temperature and wind speed can strongly influence air quality and these variables often show clear annual cycles. It is therefore common to analyse atmospheric pollutants within a seasonal framework. However, the commonly used seasons in Australia do not align well with all of the most important annual weather patterns that influence air quality in the Sydney Basin. We used Indigenous perspectives on ‘seasons’ as identified by the co-authors and combined these with statistical analysis of the local climatology. This enabled us to create a set of locally informed ‘quasi-seasons’ that we named IKALC-seasons (Indigenous Knowledge Applied to Local Climatology). Engaging with the IKALC-seasons improved our understanding of temporal variability of air pollution in western Sydney, mainly due to a better identification of the time of year when cold, still weather conditions result in higher levels of fine particulate pollution, carbon monoxide and nitrogen oxides. Although the IKALC seasons identified in this study are intrinsically local in nature, the methodology developed has broadscale application. This approach can be used to identify the times of year when micrometeorological conditions are most likely to drive poor air quality thereby helping to inform effective decision-making about emission controls.

Considering an integrated approach to assess all of the measured pollutants in a diurnal, monthly, seasonal, and annual time scales and understanding the mechanisms hidden under low air quality conditions are essential for tackling potential air pollution issues. Konya, located in central Anatolia, is the largest province of Turkey with a surface area of 40,838 km2 and has different industrial activities. The lack of recent detailed studies limits our information on the underlying air pollution levels in Konya and obscuring policymakers to develop applicable mitigation measures. In this study, we used hourly monitored air quality data of CO, NO2, NOx, PM10, PM2.5, and SO2 from five stations in Konya and investigated the temporal and spatial variabilities for the 2008–2018 period via statistical analysis. Upon analysis, particulate matter was found to be the dominant pollutant deteriorating the air quality of Konya. The highest 2008–2018 periodic mean value of PM10 was found in Karatay Belediye as 70.5 µg/m3, followed by 67.4 µg/m3 in Meram, 58.7 µg/m3 in Selçuklu, and 43.7 µg/m3 in Selçuklu Belediye. The 24-h limit value of PM10 given as 50 µg/m3 in the legislation was violated in all of the stations, mainly during winter and autumn. High positive correlations were found among the stations, and the highest correlation was obtained between Selçuklu Belediye and Karatay Belediye with a Pearson correlation coefficient of 0.77. Long-term data showed a decreasing trend in PM10 concentrations. Diurnal variability is found to be more pronounced than weekly variability. For almost all of the pollutants, except for photochemical pollutants like O3, a prominent result was the nighttime and morning rush hours high-pollutant levels. A case study done for the January 29, 2018 to February 05, 2018 episode showed the importance of meteorology and topography on the high levels of pollution. Limitation of the pollutant transport and dilution by meteorological conditions and the location of Konya on a plain surrounded by high hills are believed to be the main reasons for having low air quality in the region. [ABSTRACT FROM AUTHOR]

High-resolution modeling approaches are becoming a necessary step for enhancing regional air quality monitoring and forecasting. This study first evaluates the meteorological driver of the WRF- PMCAMx 1 × 1 km2 SmartAQ air quality prediction system and then assesses the impact of meteorological errors on the PM 2.5 predictions. The comprehensive multi-variable verification on several timescales appraises the suitability of such a high-resolution modeling system in predicting the surface weather parameters in a major coastal urban area while gaining valuable insights into the model performance. The highest agreement between the observed and modelled values has been found for the actinometric and the thermodynamic variables, with the exception of the soil moisture which was systematically overestimated. WRF largely captures the variability of the observed values for the dynamic variables, showing better skill in the warm season, with a systematic overestimation though. The overall ability in predicting the rain events, important for the wet deposition of atmospheric pollutants is approximately 50%. Wind speed forecast errors contribute to the PM 2.5 prediction errors, regardless of season and day/night hours. The impact of the wind errors is larger at almost all stations in the cold season when local emissions are elevated. • An annual multi-variable evaluation of km-scale SmartAQ WRF forecasts is presented. • The largest errors occur for wind speed and soil moisture, both overestimated. • The impact of wind speed errors on the PMCAMx PM 2.5 predictions is assessed. • Rising PM 2.5 prediction errors are correlated with rising wind prediction errors. • Meteorology has a higher impact on PM 2.5 concentrations in the cold season. [ABSTRACT FROM AUTHOR]

Abstract A multi-scale correlation analysis method is introduced and a combined analysis of multi-scale correlations between air quality and meteorology in Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA) of China is performed using 3-yr daily series of particulate matter (PM) fractions and O 3 observations and collocated meteorological reanalysis data during 2015–2017. PM and O 3 have significant seasonal oscillation and oscillations of 2–3 days, 5–7 days, quasi two weeks, and 20–30 days, which are typical periods in east-west wind (U), north-south wind (V), wind speed (WS), temperature (T), relative humidity (RH), 925-hPa specific humidity (Q), and surface air pressure (SP), although different types of meteorological parameters have different significant periods. In GHMGBA, the East Asian Monsoon has different significant impacts on PM and O 3. The relationships of air quality with meteorology on higher frequency modes are usually very different from those on lower frequency modes since different mechanisms make effects. Correlation details may also differ on different synoptic scales. Especially, PM has consistent significant negative correlations with V, T, RH, and Q on seasonal oscillation but has large positive (negative) correlations with V, T, and Q (WS and SP) on oscillations of 2–3 days, 5–7 days, and quasi two weeks in winter half year. The latter synoptic correlations reflect the impact of the typical quasi-stationary front and cold front activities on PM, which usually includes two sequential asymmetric processes: a long no-precipitation stagnation period when ventilation, dilution, and hygroscopic growth make effects; and a short precipitation period when ventilation, dilution, and wet deposition make effects. In a word, local synoptic scales correlations cannot be directly extended to estimate the response of air quality to climate change projections. Highlights • We offered a method for combined multi-scale air quality-meteorology correlations. • We confirmed the different scale sensitivity of PM to meteorology in GHMGBA. • Synoptic sensitivity should be carefully extended to climate change impacts. [ABSTRACT FROM AUTHOR]

In this study, we leverage multiple linear regression and quantile regression combined with a novel deep learning tool (SHapley Additive exPlanations) to isolate the impact of meteorology on surface ozone pollution and to assess the effectiveness of emission reduction measures across the Contiguous United States (US) during the latest climate period (1991–2020). The findings demonstrate that all regions except the Northern Rockies and the Southwest experienced decreasing trends in median values during the warm season, with rural stations in the Southeast and urban stations in the Northeast experiencing the greatest declines of −1.29 ± 0.07 and −0.85 ± 0.08 ppb.a−1, respectively. Similar to the original data, the median values of adjusted MDA8 (Maximum Daily 8-h Average) ozone show negative trends in all regions except for Southwest urban stations, with the highest recorded in rural stations of the Southeast (−1.13 ± 0.05 ppb.a−1) and urban stations of the Northeast (−0.79 ± 0.06 ppb.a−1). In addition, the 95th percentile values of original and adjusted MDA8 ozone decreased in all regions in which Northeast urban stations had the greatest reduction (original: 3.53 ± 0.29 ppb.a−1, adjusted: 2.96 ± 0.27 ppb.a−1). Our results suggest that meteorological inter-annual variability reduces the ozone burden during the warm season in the eastern US and southern California; at the same time, it contributes to increased ozone pollution in the central US, Southwest, and northern California, indicating that efforts to reduce air pollution may be hindered by climate change. Our analysis of the impact of short-term exposure to ozone on health shows that the South was the most positively impacted by emission control policies implemented after 2000, and the Northeast had the highest number of prevented deaths (30.45 deaths prevented/million people) resulting from respiratory diseases. The results of this study should benefit air quality managers and policymakers, particularly in their efforts to update ozone mitigation strategies. • We assessed original and adjusted ozone trends across CONUS during 1991–2020. • MLR, quantile regression, and deep learning (SHAP) methods were used. • The Northeast and Southeast regions experienced the highest negative trends. • The South region had the highest benefit from emission control policies after 2000. • The Northeast region had the most prevented deaths. [ABSTRACT FROM AUTHOR]

This study focuses on the changes over time in the relationship between surface temperature and particulate matter (PM) concentration over Seoul using long-term observational data. Correlation coefficients between the daily mean PM10 concentration and surface temperature were calculated to investigate the relationship between the two. The PM10 and temperature displayed a strong positive correlation, suggesting the increase in PM was driven by large-scale synoptic patterns accompanying such high temperatures. It was found that the correlation coefficient in 2002−2009 was significantly higher than that of 2010−2017, indicating that the relationship between PM10 concentration and temperature has weakened over time in recent decades. Correlation coefficients between daily averaged temperature and the PM10 of each year were calculated to account for the decreased correlation in the most recent decade. We found that the correlation coefficients between surface temperature and PM of each year exhibited a clear negative correlation with the longitudinal position of the Siberian High, suggesting that the position of the Siberian High might affect the strength of the relationship between PM concentration and temperature over Seoul. We also found that the eastward shift of the Siberian High reduces the standard deviation of pressure over Seoul, indicating reduction of synoptic perturbation. These results imply that the eastward shift of the Siberian High in recent decades might weaken the relationship between the PM and surface temperature over Seoul. This study suggests that the relationship between PM and meteorological variables is changing over time through changes in large climate variability.

The Department of Environment Affairs (DEA) recently (December 2012) published, for public comment, guidelines for regulatory air quality modelling, including guidance on modelling input requirements, and protocols and procedures to be followed to ensure comparability of results. Due to the relative scarcity of site- representative meteorology, the proposed Regulations propose mainly qualitative criteria for the acceptable use of alternative meteorology, that is meteorology representative of the site but not local to it. Modellers are also not required to validate their modelling but are required to use ambient air quality measurements to evaluate modelling results but only if these measurements are available. We explore the comparability in modelled concentrations when using different sets of surface and upper air data, using a case study of SO emissions from a large stationary source (an oil refinery), 2 and compare AERMOD modelled concentrations against monitored values. Cape Town International Airport (CTIA) surface data, 14km from the refinery, differed significantly from that of local meteorological stations. AERMOD modelled SO concentrations based on CTIA surface and upper air data differed significantly from those based on local (Table View) data. Good agreement with monitored ambient concentrations and is achieved using the combination of local (Table View) surface meteorology and Lakes Environment's Upper Air Estimator using both Table View and CTIA surface data to estimate mixing heights

Air quality in the Mexican cities of Monterrey, Nuevo Leon, and Mexicali, Baja California, has suffered great detriment in recent years. It is well known that meteorology is one of the main factors affecting the dynamics of pollutants in the atmosphere. Here, the Penn State/NCAR Meteorological Mesoscale Model (MM5) meteorological system was applied to identify meteorological conditions conducive to high-ozone concentrations in such regions. Two summer 2001 ozone episodes for each geographical domain were selected with the aid of a classification and regression tree analysis technique. Model response to changes in its physical parameterization, horizontal grid resolution, and data assimilation schemes were assessed. Once a suitable configuration was selected, performance statistics were computed for model validation. MM5 simulated satisfactorily the meteorology of such episodes, yielding indexes of agreement of 0.4–0.8 for wind speed and 0.67–0.95 for temperature, on average. However, MM5 tended to underestimated temperature and overestimated wind speed. Froude numbers were calculated to analyze the impact of the terrain complexity on wind circulation. It was concluded that in both cities, wind convergence zones might enhance high-ozone concentrations. These results improve our understanding of the atmospheric processes exerting effect on air pollution within these airsheds. [ABSTRACT FROM AUTHOR]

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 1–10ppb 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 ±0.1–1μgm−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. [Copyright &y& Elsevier]

This research explored the air pollution meteorology of the small Central Otago town of Alexandra, New Zealand. Alexandra suffers from very poor air quality during winter resulting from the high use of solid fuels for domestic heating during atmospheric conditions that are not conducive to dispersion. An existing one and a half year dataset provided the basis for analysing the relationships between PM10, temperature and wind speed to develop a predictive model. The results show that sub-freezing temperatures and wind speeds below 0.5 m s-1 at 0800 or 0900 hours can be used to predict high levels of PM10 that evening. However, these thresholds only accounted for approximately 60% of the high pollution days. Case studies revealed that the air pollution meteorology in Alexandra is complicated by the interaction of various scales of drainage flow, the occurrence of hoar frost, and possible variation in domestic heating emissions. On a daily basis three distinct and very high peaks of PM10 occur: one is mid-morning and the other two are in the evening. Further investigation is required to determine the origin of these peaks as there are implications for air quality management in the region. [ABSTRACT FROM AUTHOR]

Land use and pollutant emission changes can have significant impacts on air quality, regional climate, and human health. Here we describe a modeling study aimed at quantifying the potential effects of extensive changes in urban land cover in the New York City (NYC), USA metropolitan region on surface meteorology and ozone (O3) concentrations. The SLEUTH land-use change model was used to extrapolate urban land cover over this region from “present-day” (ca. 1990) conditions to a future year (ca. 2050), and these projections were subsequently integrated into meteorological and air quality simulations. The development of the future-year land-use scenario followed the narrative of the “A2” scenario described by the Intergovernmental Panel on Climate Change (IPCC), but was restricted to the greater NYC area. The modeling system consists of the Penn State/NCAR MM5 mesoscale meteorological model; the Sparse Matrix Operator Kernal Emissions processing system; and the US EPA Community Multiscale Air Quality model, and simulations were performed for two 18-day episodes, one near-past and one future. Our results suggest that extensive urban growth in the NYC metropolitan area has the potential to increase afternoon near-surface temperatures by more than 0. 6°C and planetary boundary layer (PBL) heights by more than 150m, as well as decrease water vapor mixing ratio by more than 0. 6gkg−1, across the NYC metropolitan area, with the areal extent of all of these changes generally coinciding with the area of increased urbanization. On the other hand, the impacts of these land use changes on ozone concentrations are more complex. Simulation results indicate that future changes in urbanization, with emissions held constant, may lead to increases in episode-average O3 levels by about 1–5ppb, and episode-maximum 8h O3 levels by more than 6ppb across much of the NYC area. However, spatial patterns of ozone changes are heterogeneous and also indicate the presence of areas with decreasing ozone concentrations. When anthropogenic emissions were increased to be consistent with the extensive urbanization in the greater NYC area, the O3 levels increased in outer counties of the metropolitan region but decreased in others, including coastal Connecticut and the Long Island Sound area. [Copyright &y& Elsevier]

Detailed analysis of particle measurements in the Lower Fraser Valley of British Columbia has provided process-related explanations for the elevated PM2.5 observations. The meteorological conditions from 24–30 August contributed to the build-up of particles including concentrated layers aloft. During this period, seven PM2.5 peaks were “diagnosed” based upon their origin, age and/or processes. Of interest were the peaks in the late evening of 26 August (expected large secondary organic contribution), 29 August in the early afternoon (oldest particles during the period mixed with fresh combustion organics) and two peaks on 30 August. These latter two peaks showed elevated sulphate and were less than 3h apart. The first was related to heterogeneous production and the second homogeneous production and nucleation. The type of analysis presented in this paper helps to identify unique situations for more detailed process studies. Successful interpretation of the observed PM2.5 required detailed meteorological analyses and was only possible with hourly or better mass quantification and size-resolved chemical characterization of the particles plus vertical aerosol backscatter profiles from LIDAR. This demonstrates the value of co-locating these approaches in the study of the processes influencing tropospheric aerosols. [Copyright &y& Elsevier]

Alternate approaches for estimating deposition of acidifying compounds by fog are needed. This paper presents empirical models for estimating concentration of SO42− in fog at high elevation locations in eastern North America. They are based upon meteorological predictors, pollutant emissions data and ambient ozone measurements and were developed using fog measurements from the chemistry of high elevation fog (CHEF) and the mountain cloud chemistry program (MCCP). Optimum models have a relative error (RE) of 0.37, indicating that the variance in the fog SO42− observations could be reduced by 63%. In addition to meteorological observations from nearby airports, the best models also utilized information on air mass history determined from back-trajectories. Ozone, measured at the fog collection site, was also found to be a potentially useful predictor of SO42− in fog. The best models performed as well as models developed from on-site meteorological measurements, indicating that it is possible to estimate fog SO42− from readily available meteorological data. [Copyright &y& Elsevier]

Fumigation under sea-breeze conditions is a major feature of the air pollution meteorology in the coastal industrial region of Kwinana, south of Perth in Western Australia. An intensive field experiment on fumigation was carried out in the region in early 1995 with the objective of using the measured data to develop and test a shoreline fumigation model. Fumigation of plumes from the Kwinana Power Station was studied using an instrumented research aircraft, radiosonde balloons, meteorological stations, a lidar, a mobile surface sampler, and sonic anemometers. The study has yielded a detailed and high quality data set as a result of both the range of observations undertaken and of the regularity of the sea-breeze conditions under scrutiny. The details of the experiment are summarised in this paper and some typical results are presented. [ABSTRACT FROM AUTHOR]

This study discusses the concentration characteristics of PM 2.5 and PM 10 , as well as pollution meteorology in large-scale and long-term heavy pollution in the Beijing-Tianjin-Hebei region and its surrounding areas from November 23 to December 4, 2018, where the primary pollutants are comprised of PM 2.5 and PM 10 . The monitoring results obtained from ground-based and vehicle-mounted lidars, as well as the HYSPLIT-4 backward trajectory combined with meteorological factors analysis are discussed. The accuracy and uncertainty of the air quality forecast model of NAQPMS, CMAQ, and CAMx during heavy air pollution were analyzed retrospectively. The results show that PM 2.5 and sand dust in most cities in the south-central region contribute to severe pollution levels. The hourly peak concentrations of PM 10 in Zhangjiakou, Beijing, Shijiazhuang, Handan, and Zhengzhou were 1589, 864, 794, 738, and 766 μg·m -3 , respectively. The respective hourly peak concentrations of PM 2.5 were 239, 319, 387, 321, and 380 μg·m -3 . Ground static pressure field, high humidity, inversion, and other static and stable conditions, as well as sand dust transmitted from the northwest, were important pollution meteorological and weather factors. The monitoring data of ground-based lidar and vehicle-mounted lidar combined with the HYSPLIT-4 backward trajectory analysis showed that the air pollutant transmitted from the Southwest and Southeast during the heavy pollution period was primarily PM 2.5 . The air pollutant transmitted from the Northwest during the two sand dust processes. Moreover, the model of NAQPMS, CMAQ, and CAMx performed well in forecasting the heavy pollution process in the Beijing-Tianjin-Hebei region and its surrounding areas. However there are slight deviations for some individual cities, related to uncertainty in the meteorological model prediction, atmospheric chemical reaction mechanism, and pollution source list. Furthermore, the reduction in pollution source emissions caused by pollution emergency measures was also one of the main reasons for the overestimation.

This study focuses on the changes over time in the relationship between surface temperature and particulate matter (PM) concentration over Seoul using long-term observational data. Correlation coefficients between the daily mean PM10 concentration and surface temperature were calculated to investigate the relationship between the two. The PM10 and temperature displayed a strong positive correlation, suggesting the increase in PM was driven by large-scale synoptic patterns accompanying such high temperatures. It was found that the correlation coefficient in 2002–2009 was significantly higher than that of 2010–2017, indicating that the relationship between PM10 concentration and temperature has weakened over time in recent decades. Correlation coefficients between daily averaged temperature and the PM10 of each year were calculated to account for the decreased correlation in the most recent decade. We found that the correlation coefficients between surface temperature and PM of each year exhibited a clear negative correlation with the longitudinal position of the Siberian High, suggesting that the position of the Siberian High might affect the strength of the relationship between PM concentration and temperature over Seoul. We also found that the eastward shift of the Siberian High reduces the standard deviation of pressure over Seoul, indicating reduction of synoptic perturbation. These results imply that the eastward shift of the Siberian High in recent decades might weaken the relationship between the PM and surface temperature over Seoul. This study suggests that the relationship between PM and meteorological variables is changing over time through changes in large climate variability. [ABSTRACT FROM AUTHOR]

An abstract of the article "Modelling emission trends from non-constant time series of PM10 concentrations in Christchurch, New Zealand," by Tim Appelhans, Andrew Sturman, and Peyman Zawar-Reza is presented.