Hourly Monitoring Data Research Articles

Determination of Fine Particulate Matter Episode Periods in Jakarta by Incorporating Meteorological Conditions

The analysis of observed fine particulate matter (PM2.5) in Jakarta, Indonesia, was conducted from January 2021 to October 2023 using hourly monitoring data from AirNow, managed by the U.S. Environmental Protection Agency. Data were collected from two air quality monitoring stations in Central and South Jakarta. Meteorological data, including wind speed, temperature, humidity, and rainfall, were obtained from NASA's global forecasting database. The objective was to understand the correlation between PM2.5 formation and meteorological conditions to identify favorable meteorology during high air pollution episodes. During the dry season (May to August), there was a notable increase in PM2.5, linked to higher temperatures, lower humidity, slower wind speeds, and minimal rainfall. The results showed that meteorological conditions significantly affected PM2.5 concentrations, influenced by seasonal changes. In contrast, the rainy season had lower PM2.5 concentrations due to higher rainfall, humidity, and wind speed, and lower temperatures. Further modeling studies are required to assess the combined impacts of emission build-up and meteorology on recurring PM2.5 episodes in Jakarta.

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands.

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016–2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50–0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24–0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays’. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35–0.70; PM2.5 hourly R2 = 0.01–0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

Analysis of PM2.5 Characteristics in Yancheng from 2017 to 2021 Based on Kolmogorov–Zurbenko Filter and PSCF Model

Based on the hourly monitoring data including meteorological elements and PM2.5 mass concentration in Yancheng from 2017 to 2021, PM2.5 mass concentration variations, influencing factors and source apportionment were studied by the Kolmogorov–Zurbenko filter and Potential Source Contribution Function Analysis (PSCF) method. The results showed that the mass concentration of PM2.5 in Yancheng showed a decreasing trend from 2017 to 2021, with a decline rate of about 33.8% (2017, 44.79 ± 31.22 μg/m3; 2021, 29.66 ± 21.69 μg/m3); the visibility increased by 18.4% (2017, 11.69 ± 6.46 km; 2021,13.8 ± 6.24 km), which is mainly related to emission reduction measures in China. The mass concentration of PM2.5 has significant seasonal variation characteristics, with the highest in winter, reaching 60.61 μg/m3, and the lowest in summer, only 23.11 μg/m3. The diurnal variation of PM2.5 showed a unimodal distribution, and concentration difference is obvious under the influence of land–sea breeze (36.60 μg/m3, easterly wind; 43.57 μg/m3, westerly wind). Meteorological factors have an important impact on the mass concentration of PM2.5, which fluctuates with seasons. It is calculated to have a good fitting relationship between the visibility and PM2.5 concentration, and the correlation decreases with the increase in humidity (−0.71 ~ −0.41). The relatively clean atmosphere under high humidity conditions is also prone to the obstruction to vision. The corresponding PM2.5 concentration varies significantly under different wind directions and wind speeds in Yancheng, and high values mainly come from the northwest–southeast–southwest direction. The potential source regions in autumn are mainly distributed in southwestern Jiangsu and northwestern Zhejiang; the potential source regions in winter are mainly located in southwestern Jiangsu, southern Anhui and northern Jiangxi.

Assessment of 24-h moving average PM2.5 concentrations in Bangkok, Thailand against WHO guidelines

Particulate matter less than 2.5 μm in diameter (PM2.5) pollution has gained more concern, as can be seen from the World Health Organization (WHO) revised air quality guideline (AQG) value. To compare with the 24-h AQG value, the 24-h mean (daily average) of hourly monitoring data has been widely used, but the nature of data in hourly variation has been lost. The 24-h moving average can be an alternative approach to preserving hourly data behavior, while various positions of the hour (leftmost, center, or rightmost hour) can be used to store the moving average. In this study, similarity testing by cross-correlation and Euclidean distance was performed to present a suitable 24-h moving average time series for hourly data, and then presented proportions of PM2.5 concentrations within each interim target and AQG ranges. The PM2.5 time series of the leftmost and rightmost 24-h moving average revealed lags of − 13 to − 10 h and 10 to 13 h for the observed hourly data, respectively. The lags of the center 24-h moving average time series were of − 2 to 1 h, which showed more similar events to the hourly PM2.5 fluctuation than the leftmost and rightmost time series. The center 24-h moving average concentrations measured in Bangkok were categorized into interim target and AQG ranges. The results revealed that the proportion of concentrations lower than AQG during nighttime and daytime was approximately 40 and 28%, respectively. However, the results given from the leftmost and rightmost time series showed time shifts and differences in diurnal variation caused by the position used to store the average value. To keep hourly PM2.5 variation that can be compared with the 24-h WHO guidelines, this should be performed with careful and precautionary consideration of misinterpretation over a time scale, especially for implementing countermeasures and policies.

Characteristics and Weekend Effect of Air Pollution in Eastern Jilin Province

Using the hourly monitoring data of pollutants from 16 automatic atmospheric monitoring stations in eastern Jilin Province from 2015 to 2020, this paper analyzed the temporal and spatial distribution laws of CO, SO2, NO2, PM10, PM2.5, and O3 in eastern Jilin Province. At the same time, the regional transport pathways of pollutants were analyzed using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model; the potential source contribution function (PSCF) analyzed the potential source area of PM2.5. Finally, the “weekend effect” of CO, NO2, PM2.5, and O3 was analyzed. The results showed that the six pollutants showed a downward trend year by year. The concentrations of O3, PM10, and PM2.5 were higher in northwest Jilin, and the concentrations of SO2 and CO were higher in southwest Jilin. Except for CO, the seasonal variation of pollutants was pronounced. Except for O3, most pollutants had the highest concentration in winter. Hourly variation analysis described that SO2 and O3 had only one peak in a day, and the other four pollutants showed “double peak” hourly variation characteristics. The study area was mainly affected by the airflow pathway from northwest and southwest. The weight potential source contribution function (WPSCF) high-value area of PM2.5 was northwest and southwest. O3 showed a “negative weekend effect”, and NO2 and CO showed a “positive weekend effect”.

Distinguishing Air Pollution Due to Stagnation, Local Emissions, and Long-Range Transport Using a Generalized Additive Model to Analyze Hourly Monitoring Data

Distinguishing Air Pollution Due to Stagnation, Local Emissions, and Long-Range Transport Using a Generalized Additive Model to Analyze Hourly Monitoring Data

Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.

Comparative analysis of the variation characteristics of O3, NO2 and CO in the boundary layer in coastal cities and inland cities in the middle latitude regions in China

Based on the hourly monitoring data of O3 concentration, and its closely related NO2 and CO concentration in four coastal cities (Qingdao, Yantai, Weihai, Rizhao) and four inland cities (Dezhou, Liaocheng, Heze and Jining) in Shandong Province from 2009 to 2017, we compared and analyzed variations of O3, NO2 and CO pollutants in the boundary layer in both coastal and inland cities in mid-latitude regions, and explored their causes. It is found that O3 pollution showed an aggravating tendency in coastal and inland cities from 2012 to 2017, especially remarkably in inland cities. However, NO2 and CO pollution showed an opposite tendency. Seasonal variation of O3 pollution in the boundary layer of coastal cities in the mid-latitude showed an M-shaped bimodal structure, similar to that in low latitude cities, while NO2 and CO show a V-shaped structure with a maximum in winter and a minimum in summer. The diurnal variation of O3 concentration is featured with a high concentration in the daytime and a low one in the night, while NO2 and CO concentration show a bimodal curve with a high concentration both in the morning and evening.

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO2, NOx, O3, SO2 and particulate air pollutants PM2.5, PM10) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the “ADDRESS” independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO2 concentration level by incorporating wind-related variables into LUR model development.

Spatial and Temporal Characteristics and Main Contributing Regions of High PM2.5 Pollution in Hong Kong

ABSTRACTFine particulate matter (PM2.5) pollution episodes occur frequently in most Chinese metropolises. Because Hong Kong is affected by the East Asian monsoon, its high PM2.5 pollution is potentially contributed to by a combination of local and regional emissions. This study identified the spatial and temporal characteristics and contributing regions of PM2.5 pollution episodes in Hong Kong. We analyzed the hourly monitoring data collected at various monitoring stations in Hong Kong for the year 2014 in addition to spatial distributions of PM2.5 in China in the days preceding high pollution episodes, and we identified the spatial and temporal characteristics of high PM2.5 pollution in Hong Kong. The relative contributions of local and regional sources to high PM2.5 episodes were then quantified. Finally, backward trajectories of air masses arriving in Hong Kong during high PM2.5 episodes were generated to determine the main contributing regions. The results indicated that PM2.5 levels were higher in the northwestern and southeastern parts of Hong Kong and the Yuen Long station observed the most serious pollution, with a highest daily average PM2.5 concentration of 118 µg m–3; other areas noted relatively lower concentrations, with maximum daily average concentrations less than 100 µg m–3. Regional sources accounted for almost 70% of the PM2.5 pollution and were mostly from the northern and northeastern areas of Hong Kong. Six main contributing regions were identified: the northern region of Guangdong province, Henan–Shandong, the Yangtze River Delta, Jiangxi, Fujian, and Anhui-Hubei. Among these, Guangdong province contributed the highest proportion of the pollution. Because PM2.5 is a major air pollutant in Hong Kong, the findings of this study will be useful for forecasting PM2.5 pollution episodes. Moreover, the developed methodology can be extended to other pollutants if their characteristics and main contributing regions can be identified.

Urban air quality evaluations under two versions of the national ambient air quality standards of China

The air quality of urban areas is of a great concern for residents living in cities. China has released a new National Ambient Air Quality Standard (NAAQS) in 2012 to improve the air quality evaluation index because the reported air quality situation from governments is inconsistent with public sensing. In total, 190 cities' hourly monitoring data are publicized in the national web platform. By using the above data, the air pollution indexes (APIs) under the previous NAAQS and air quality indexes (AQI) for all 190 cities' were calculated. With the new NAAQS, the national attainment rate has fallen from 73.63% to 59.62% with regional differences. 8 of the 10 cities with the worst air quality are located in North China around Beijing, while 9 of the 10 cities with the best air quality are located in South China. These results indicate that cities in North China, including Beijing, need to address the air pollution together as the pollution in North China is a regional issue and not a local issue.

Mean Imputation Techniques for Filling the Missing Observations in Air Pollution Dataset

Almost all real life datasets consist missing values. These are usually due to machine failure, routine maintenance, changes in siting monitors and human error. The occurence of missing values requires special attention on analysing the data. Incomplete datasets can cause bias due to systematic differences between observed and unobserved data. Therefore, the need to find the best way in estimating missing values is very important so that the data analysed is ensured of high quality. In this research, three types of mean imputation techniques that are mean, mean above and mean above below methods were used to replace the missing values. Annual hourly monitoring data for PM10 were used to generate missing values. Four randomly simulated missing data were evaluated in order to test the efficiency of the methods used. They are 5%, 10%, 15%, 25% and 40%. Three types of performance indicators that are mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) were calculated to describe the goodness of fit for all the method. From all the method applied, it was found that mean above below method is the best method for estimating data for all percentages of simulated missing values.

Performance Assessment and Prediction of Welded Joints in Orthotropic Decks Considering Hourly Monitoring Data

Orthotropic steel decks can experience fatigue at welded joints, the assessment of which turns out to be a complex task owing to their intricate geometry, the stochastic nature of the primary live load (traffic flow) and the temperature-dependent composite action between the pavement and the steel deck. In recent years, the possibility of monitoring, in addition to traditional inspections, has been put forward as a means of improved assessment. Nevertheless, a rigorous framework to (a) enable the effective use of high amounts of multiple/incomplete data provided by distributed data acquisition systems, (b) improve current monitoring-based assessment methods, and (c) enhance current simulation and data visualization techniques, is still absent. A theoretical framework is presented in which a stress-related performance indicator is estimated through a multiple regression model with hourly pavement temperatures and heavy traffic intensities as independent variables. The proposed performance indicator is proportional to fatigue damage following the principles of the S–N approach and the Miner’s rule. Typical applications of this model include (a) analysis of monitoring outcomes for performance assessment, (b) performance prediction of past/future events and (c) fatigue assessment. To illustrate the proposed approach, model-based performance predictions are benchmarked with real monitoring outcomes from the Great Belt Bridge (in Denmark), and good agreement has been observed. Moreover, model predictions are used to estimate the fatigue life of a monitored welded joint. The new methodology enhances Structural Health Monitoring (SHM) methods for orthotropic decks and provides a framework to integrate and visualize the multiple outcomes produced by modern monitoring systems as a part of the Bridge Management System or to assess the remaining life of structures.

Secondary Organic Carbon Estimations with Hourly Monitoring Data and the OC/EC Primary Ratio Method

This study examined the hourly monitoring data from 2006 to 2009 collected by the Aerosol Supersite of the Environmental Protection Administration of Taiwan. The OC/EC primary ratio method has been applied to estimate the content of secondary organic carbon (SOC). Results of this study indicated that the monthly concentrations of PM2.5, OC, and EC all remained low in summer but went up in winter. Possible factors were climate-related and influences from continental high pressure systems. The content (24–36%) of SOC in summer was significantly higher than in other seasons, indicating that a great formation of organic carbon in summer. When considering the hourly trend, apparent peaks can be consistently observed in the morning, which may be due to an increase of mobile pollution source and photochemical reactions. (OC/EC)min ratio values were calculated based on both hourly and daily concentrations of OC and EC, then annual values (2006~2009) were 0.20~1.11 and 0.68~2.72 for hourly and daily data base respectively. Content of SOC in PM2.5 and OC were estimated to be 16~23 % and 75~93 % based on (OC/EC)min ratio from hourly data set, and were 11~18 % and 42~77 % based on (OC/EC)min ratio from daily data set. Results from this study, as well as those from other studies, demonstrated that the OC/EC ratio is dependent upon the sampling method as well as the method of analysis. Furthermore, the daily OC/EC ratio may change, and significant variations may be found even within 24 hours. Taken together, when conducting estimation of SOC, it is important to eliminate the consideration on background concentrations but to take a good advantage of the high temporal resolution of hourly monitoring data in order to estimate SOC using a corrective approach.

Illustrating the benefit of using hourly monitoring data on secondary inorganic aerosol and its precursors for model evaluation

Abstract. Secondary inorganic aerosol, most notably ammonium nitrate and ammonium sulphate, is an important contributor to ambient particulate mass and provides a means for long range transport of acidifying components. The modelling of the formation and fate of these components is challenging. Especially, the formation of the semi-volatile ammonium nitrate is strongly dependent on ambient conditions and the precursor concentrations. For the first time an hourly artefact free data set from the MARGA instrument is available for the period of a full year (1 August 2007 to 1 August 2008) at Cabauw, the Netherlands. This data set is used to verify the results of the LOTOS-EUROS model. The comparison showed that the model underestimates the SIA levels. Closer inspection revealed that base line values appear well estimated for ammonium and sulphate and that the underestimation predominantly takes place at the peak concentrations. For nitrate the variability towards high concentrations is much better captured, however, a systematic relative underestimation was found. The model is able to reproduce many features of the intra-day variability observed for SIA. Although the model captures the seasonal and average diurnal variation of the SIA components, the modelled variability for the nitrate precursor gas nitric acid is much too large. It was found that the thermodynamic equilibrium module produces a too stable ammonium nitrate in winter and during night time in summer, whereas during the daytime in summer it is too unstable. We recommend to improve the model by verification of the equilibrium module, inclusion of coarse mode nitrate and to address the processes concerning SIA formation combined with a detailed analysis of the data set at hand. The benefit of the hourly data with both particulate and gas phase concentrations is illustrated and a continuation of these measurements may prove to be very useful in future model evaluation and improvement studies. Based on our findings we propose to implement a monitoring strategy using three levels of detail within the Netherlands.

Spatial and temporal variability of fecal indicator bacteria in an urban stream under different meteorological regimes

As a representative urban stream in Korea, the Gwangju (GJ) stream suffers from chronic fecal contamination. In this study, to characterize levels of fecal pollution in the GJ stream, the monthly monitoring data for seven years (from 2001 to 2007) and the hourly monitoring data from two field experiments were examined with respect to seasonal/daily variations and spatial distribution under wet and dry weather conditions. This research revealed that concentrations of fecal indicator bacteria strongly varied depending on the prevalent meteorological conditions. That is, during the dry daytime, fecal indicator bacteria concentrations decreased due to inactivation from solar irradiation, but rapidly increased in the absence of sunlight, suggesting external source inputs. In addition, bacterial concentrations substantially increased during rainfall events, due probably to a major contribution from combined sewer overflow. The observations in this study can be useful for implementing fecal pollution management strategies and for predicting fecal contamination as a function of meteorological conditions.

Determinants of ultrafine particle exposures in transportation environments: findings of an 8-month survey conducted in Montréal, Canada

An 8-month sampling campaign was conducted in Montréal, Canada to explore determinants of ultrafine particle (UFP) exposures in transportation environments and to develop models to predict such exposures. Between April and November 2006, UFP (0.02-1 mum) count exposure data were collected for one researcher during 80 morning and evening commutes including a 0.5-km walk, a 3-km bus ride, and a 26-km automobile ride in each direction. Ambient temperature, relative humidity, precipitation, and wind speed/direction data were collected for each transit period and the positions of bus and automobile windows were recorded. Mixing heights were also estimated. Morning UFP exposures were significantly greater than those in the evening, with the highest levels observed in the automobile and the lowest while walking. Wind speed and mixing height were highly correlated, and as a result only wind speed was considered in multivariable models owing to the accessibility of quantitative hourly monitoring data. In these models, each 10 degrees C increase in morning temperature was associated with decreases of 14,560/cm(3) (95% CI=11,111 to 18,020), 8160/cm(3) (95% CI=5060 to 11,260), and 11,310/cm(3) (95% CI=6820 to 15,810) for UFP exposures in walk, bus, and automobile environments, respectively. Likewise, each 10-km/h increase in morning wind speed corresponded to decreases of 8252/cm(3) (95% CI=5130 to 11,360), 6210/cm(3) (95% CI=3420 to 9000), and 6350/cm(3) (95% CI=2440 to 10,260) for UFP exposures in walk, bus, and automobile environments, respectively. Similar trends were observed in the evening hours. In an evaluation of model performance, moderate correlations were observed between measured and predicted UFP exposures on new bus (r=0.65) and automobile (r=0.77) routes. Further research is required to incorporate variables such as traffic density and vehicle ventilation settings into the models presented.

Estimation of missing values in air pollution data using single imputation techniques

Air pollution data obtained using automated machines often contain missing values which can cause bias due to systematic differences between observed and unobserved data. We used interpolation and mean imputation techniques to replace simulated missing values from annual hourly monitoring data for PM10. The most effective method for generating the missing data points was to replace each missing value with the mean of the two data points before and after the missing value. This approach was referred to as the mean-before-after method.

Analysis and interpretation of the continuous hourly monitoring data for 26 C 2–C 8 hydrocarbons at 12 United Kingdom sites during 1996

A fully automatic network with 11 urban background and one rural site has been set up and operated successfully in the UK to measure, continuously, hourly concentrations of 26 C 2–C 8 hydrocarbons; the measurements collected form one of the largest data sets of urban hydrocarbon air quality data available anywhere in Europe. The hourly data have been analysed on a site-by-site and hydrocarbon-by-hydrocarbon basis by constructing scatter plots against benzene. From these scatter plots a number of important sources of hydrocarbon emissions have clearly been identified including: motor vehicle exhaust and evaporative emissions, natural gas leakage, petrol station forecourt evaporative emissions and industrial sources. Overall, urban background hydrocarbon air quality is heavily influenced by motor vehicles and the general pattern of relative concentrations has been accurately defined for the United Kingdom as ratios relative to benzene. Comparisons of these concentrations are made with data for other major cities and with European regional data.

Analysis of RADM gas concentration predictions using OSCAR and NEROS monitoring data

The ability of the Regional Acid Deposition Model (RADM) to produce realistic atmospheric trace gas concentration patterns for a variety of atmospheric conditions is explored by comparing model results with surface monitoring data. Two frontal storm system episodes, monitored in 1981 as part of the Oxidation and Scavenging Characteristics of Aptil Rains (OSCAR) experiment, and one relatively dry high-pressure-system case from the 1979 Northeast Regional Oxidant Study (NEROS) were studied. The temporal and spatial variations of SO 2, O 3 and N species monitored at selected locations throughout the eastern U.S. and Canada are compared to the corresponding RADM grid-average predictions. The analysis is complicated by the fact that grid averages are nor necessarily representative of point measurements when the measurements are strongly influenced by local subgrid-scale phenomena. The comparison database also is limited by gaps in the hourly monitoring data and ambiguity in the N species measurements. To reduce these difficulties, observational sites located in rural and suburban areas which are less influenced by large emission sources were chosen for the comparisons, sharp hourly spikes in the observational data were omitted from the analysis and nitrogen species comparisons were limited to special observations where the measurement ambiguities were less pronounced. Spatial distributions of SO 2 and O 3 daytime (10 a.m.–4 p.m.) and night-time (10 p.m.–4 a.m.) RADM concentration averages were compared to the observational averages to confirm that the model and data concentration patterns agreed over the domain. Intercomparisons of individual grid-point pair comparisons were facilitated using fractional differences, which are defined as the difference in the observations and predictions divided by the sum of the observations and predictions. Predictions and observations showed similar patterns of high and low concentrations throughout the region for all three of the events for the daytime comparisons; the lower night-time agreement improved when night-time stability adjustments were applied to the model predictions. Summarizing over all of the individual grid-point, day-total comparisons for all three events, it was found that, for O 3, all of the pairs were in agreement to better than a factor of two and 50% of them were in agreement to a factor of 1.1 or better; for SO 2, 88% of the pairs were in agreement to better than a factor of two and 25% of the pairs were in agreement to a factor of 1.1 or better. Predicted N species concentrations in the 1–4 and 5–7 ppb ranges for two OSCAR cases were comparable to the levels measured at a rural Indiana site during the OSCAR I and OSCAR IV events. Predictions of NEROS NO x in the 3–11 ppb range were comparable with the concentrations measured at a rural Pennsylvania site for a summer 1986 case meteorologically similar to the 1979 NEROS I event. In general, higher agreement is associated with well-mixed conditions in areas where local characteristics of the observational site more closely resemble the grid-average characteristics.