利用常规气象观测资料、颗粒物监测及激光雷达探测数据,采用天气学分析、空气质量轨迹追踪模型,对 2021 年3 月呼和浩特市发生的一次严重沙尘污染天气成因及传输特征进行分析.结果表明:此次污染过程有两次污染物浓度峰值,可分为两个阶段.3 月14-15 日受高空低槽和地面冷锋共同影响,呼和浩特市出现大风、扬沙、沙尘暴和强沙尘暴天气,PM2.5 在15 日03:00 升至1382 μg·m-3,严重污染时间长达15 h;3 月16-18 日高空为弱脊后西南偏西气流,地面气压场减弱,近地面扩散条件较差,发生污染物积累和沙尘回流,导致严重污染长时间持续.激光雷达数据表明,3 月14 日白天呼和浩特高空有大量细粒子聚集,15 日凌晨该地区首要污染物由PM2.5转为PM10,12:00 PM10浓度升至4099 μg·m-3,16 日沙尘回流,再次出现严重污染;17 日污染物浓度开始降低,18 日 05:00 此次沙尘天气结束.后向轨迹分析表明,蒙古国为此次严重沙尘污染的主要源区.

Based on the air quality data and conventional meteorological data of the Nanjing Region from January 2015 to December 2016, to analyze the characteristics of O3 concentration changes in the Nanjing Region, a light gradient boosting machine (LightGBM) model was established to predict O3 concentration. The model was compared with three machine learning methods that are commonly used in air quality prediction, including support vector machine, recurrent neural network, and random forest methods, to verify its effectiveness and feasibility. Finally, the performance of the prediction model was analyzed under different meteorological conditions. The results showed that the variation in O3 concentration in Nanjing had significant seasonal differences and was affected by a combination of its pre-concentration, meteorological factors, and other air pollutant concentrations. The LightGBM model predicted the ground-level O3 concentration in the Nanjing area more precisely to a large extent (R2=0.92), and the model outperformed other models in prediction accuracy and computational efficiency. In particular, the model showed a significantly higher prediction accuracy and stability than that of other models under a high-temperature condition that was more likely prone to ozone pollution. The LightGBM model was characterized by its high prediction accuracy, good stability, satisfactory generalization ability, and short operation time, which broaden its application prospect in O3 concentration prediction.

利用2013—2020年1月内蒙古呼和浩特市环境监测数据、气象观测数据、美国国家海洋和大气局(National Oceanic and Atmospheric Administration,NOAA)月平均北极涛动(Arctic Oscillation,AO)指数、欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)ERA5再分析资料等,对2020年1月呼和浩特市雾霾天气偏多的气象成因进行分析.结果表明:(1)2020年1月较弱的冬季风环流背景、高湿度条件、维持较低的边界层高度、逆温结构等是造成呼和浩特市雾霾偏多的主要原因;(2)2013—2020年1月同期呼和浩特市逐日平均相对湿度、前期累计降水量、逐小时2 min平均风速小于等于1.5 m·s-1的日数与雾霾日数均呈显著正相关,其中逐小时2 min平均风速小于等于1.0 m·s-1更利于雾霾的发生;(3)地面积雪对呼和浩特市持续雾霾天气影响较大,积雪越深、持续时间越长,则雾霾日数也越多;(4)2020年1月呼和浩特市雾霾天气持续时,边界层平均高度约430～550 m,最低约210 m,较低的边界层利于污染物在近地层集聚,导致水平能见度较低,雾霾加重,边界层高度越低,污染越重.

AMA GC5000BTX was used to monitor the mixing ratio of benzene, toluene, ethylbenzene, m,p-xylene, o-xylene, and styrene (BTESX) in the atmosphere of the northern suburb of Nanjing from January 2014 to December 2016. The temporal variation characteristics of BTESX and the influence of meteorological elements on it were analyzed, and the characteristic ratio method (T/B) was used to qualitatively analyze the source of BTESX. Finally, the human exposure analysis and evaluation method of EPA was used to evaluate the health risk of BTESX. The results showed that during the observation period, the average mixing ratio of BTESX was (7.28±6.63)×10-9, and the mixing ratio of benzene was the highest at (2.45±3.91)×10-9. The mixing ratio of other species from large to small was toluene>ethylbenzene>m,p-xylene>o-xylene>styrene, which were (2.41±2.61)×10-9, (1.37±1.28)×10-9, (0.51±0.48)×10-9, (0.3±0.36)×10-9, and (0.22±0.42)×10-9, respectively. Due to the existence of stable aromatic sources, the monthly and seasonal variation in BTESX mixing ratio was not as obvious as that of other species (NOx, CO, SO2, PM2.5, etc.). The weekend effect of BTESX and other pollutants was not significant. The mixing ratio of BTESX was largely affected by the short distance transportation of chemical enterprises and traffic trunk roads in the northeast, resulting in a large mixing ratio of BTESX in the northeast. The mixing ratio of BTESX was jointly affected by relative humidity and temperature, and its high value area was mainly located in the range of 30%-70% relative humidity. In this range of relative humidity, the high value range of BTESX volume fraction increased with the elevation of temperature. The HI (hazard index) of BTESX in different seasons was within the safety range recognized by EPA, whereas the R (carcinogenic risk of benzene) value was higher than the safety threshold specified by EPA. At the same time, the HI and R values were higher in summer, to which great attention should be paid.

In this study, 56 volatile organic compounds species (VOCs) and other pollutants (NO, NO2, SO2, O3, CO and PM2.5) were measured in the northern suburbs of Nanjing from September 2014 to August 2015. The total volatile organic compound (TVOC) concentrations were higher in the autumn (40.6 ± 23.8 ppbv) and winter (41.1 ± 21.7 ppbv) and alkanes were the most abundant species among the VOCs (18.4 ± 10.0 ppbv). According to the positive matrix factorization (PMF) model, the VOCs were found to be from seven sources in the northern suburbs of Nanjing, including liquefied petroleum gas (LPG) sources, gasoline vehicle emissions, iron and steel industry sources, industrial refining coke sources, solvent sources and petrochemical industry sources. One of the sources was influenced by seasonal variations: it was a diesel vehicle emission source in the spring, while it was a coal combustion source in the winter. According to the conditional probability function (CPF) method, it was found that the main contribution areas of each source were located in the easterly direction (mainly residential areas, industrial areas, major traffic routes, etc.). There were also seasonal differences in concentration, ozone formation potential (OFP), OH radical loss rate (LOH) and secondary organic aerosols potential (SOAP) for each source due to the high volatility of the summer and autumn temperatures, while combustion increases in the winter. Finally, the time series of O3 and OFP was compared to that PM2.5 and SOAP and then they were combined with the wind rose figure. It was found that O3 corresponded poorly to the OFP, while PM2.5 corresponded well to the SOAP. The reason for this was that the O3 generation was influenced by several factors (NOx concentration, solar radiation and non-local transport), among which the influence of non-local transport could not be ignored.

The physical and chemical characteristics of aerosols have a significant effect on scattering coefficients (asp). In this research, a single particle aerosol mass spectrometer (SPAMS) and a scanning mobility particle sizer spectrometer (SMPS) were used to measure the chemical composition and size distribution of aerosol number concentration in Nanjing, China. During the observation time, the average asp varied from 11.64 to 485.84 Mm(-1), with an average of 114.16 +/- 77.87 Mm(-1). For relative humidity greater than 75.0%, the optical hygroscopic growth factor (f(RH)) increased rapidly, with an approximate distribution of 3.41 +/- 0.78. During the night of July 15th, 2015, nitrate and sulfate increased by 5.93 and 1.32 times, respectively, compared with daytime values. The peak of the average size distribution of the aerosol number concentration during the observation period (341.0 cm(-3)) was located in the Aitken mode of 58.3 nm. After particle size conversion from the vacuum aerodynamic diameter to the mobility diameter, the particle size range of the aerosol mass spectrometer ranged from 138.2-723.4 nm, all of which are distributed in accumulation mode. Source apportionment revealed that secondary, biomass, and carbonaceous aerosols are the primary facilitators in 327.8-406.8 nm, accounting for 21.3%, 25.7%, and 33.3%, respectively. The fixed scattering coefficients were calculated using the Mie model, according to the source apportionment of aerosol number concentration for modifying the aerosol refractive index. From July 6th-11th, the calculation and observation results of asp exhibit excellent consistency and a Pearson coefficient of 0.66. Therefore, the accurate size distribution of the chemical composition and source apportionment of aerosols are of great significance for the simulation of atmospheric optical scattering.

Black carbon (BC) reduces the photolysis coefficient by absorbing solar radiation, thereby affecting the concentration of ozone (O 3 ) near the ground. The influence of BC on O 3 has thus received much attention. In this study, Mie scattering and the tropospheric Ultraviolet and Visible radiation model are used to analyze the effect of BC optical properties on radiation. Combined with data of O 3 precursors in Nanjing in 2014, an EKMA curve is drawn, and the variations in O 3 concentration are further investigated using a zero-dimensional box mechanism model (NCAR MM). When O 3 precursors are unchanged, radiation and O 3 show a highly similar tendency in response to changing BC optical properties ( R =0.997). With the increase of modal radius, the attenuation of fresh BC to radiation and O 3 first trends upward before decreasing. In the mixing process, the attenuation of BC to radiation and O 3 presents an upward tendency with the increase of relative humidity but decreases rapidly before increasing slowly with increasing thickness of coating. In addition, mass concentration is another major factor. When the BC to PM 2.5 ratio increases to 5% in Nanjing, the radiation decreases by approximately 0.13%–3.71% while O 3 decreases by approximately 8.13%–13.11%. The radiative effect of BC not only reduces O 3 concentration but also changes the EKMA curve. Compared with the NOx control area, radiation has a significant influence on the VOCs control area. When aerosol optical depth (AOD) increases by 17.15%, the NO x to VOCs ratio decreases by 8.27%, and part of the original NO x control area is transferred to the VOCs control area.

该文利用WPS、β射线测尘仪、EMS污染气体监测系统的观测数据,结合观测期间天气形势,分析了青奥会期间南京不同天气型下气溶胶数浓度和污染气体的分布特征.结果表明:观测期间气溶胶数浓度平均为7 302个/cm4,污染气体(NOx、O3、SQ、CO)平均浓度分别为23.09、55.1、8.7和867.3 μg/m.不同天气型下气溶胶粒径分布差异较大,鞍型场(Ⅱ)、副热带高压(Ⅴ)和冷涡(Ⅲ)控制下数浓度谱呈单峰型分布;大陆高压和热带气旋外围(Ⅰ)控制下数浓度谱呈双峰型分布;冷高压(Ⅳ)控制下数浓度谱呈三峰型分布.同时日浓度变化差异也大,N10-300nm在Ⅰ型、Ⅲ型、Ⅳ型呈三峰型,在Ⅱ型、Ⅴ型呈双峰型;NOx和CO在Ⅰ型分别呈三峰型和单峰型,其余都是双峰型;O3一直呈单峰型.

To investigate the seasonal variation and characterization of water-soluble ions (WSIs) present in airborne particle deposition (APD) during Haze Days (visibility ≤7.5 km) and Normal Days (visibility >7.5 km) in suburban Nanjing area, 151 filter samples were collected from 18 May 2013 to 26 May 2014. Ten different WSIs from the samples were determined by Ion Chromatography. The results indicated that secondary WSIs (NH4 +, NO3 −, and SO4 2−) were the main ions in the WSIs, averaging 17.2, 18.5, and 17.1 μg/m3, respectively, and accounting respectively 20.9, 22.5, and 20.8% of the total WSIs. On Haze Days, the concentration of WSIs increased dramatically in fine size (particle size <2.1 μm), especially for NH4 +, NO3 −, and SO4 2− (increased by 52.6, 71.3, and 73.1%, respectively), whereas the concentrations of WSIs increased slowly in coarse size (2.1 μm < particle size < 10 μm), in which NH4 +, NO3 −, and SO4 2− increased by 14.7, 27.2, and 54.5%, respectively. According to the backward trajectories and the principal component analysis analysis, Nanjing APD were mainly derived from the soil dust in northern China (35%) in the spring, from ocean air masses (61 and 55%) in the summer and the autumn, and from local air masses (73%) in the winter. On summer Haze Days, secondary components in PM2.1 consisted mainly of (NH4)2SO4 and NH4NO3, whereas secondary components in PM2.1–10 consisted mainly of (NH4)2SO4, NH4Cl, and NH4NO3. The increasing concentrations of secondary components increase the light extinction coefficients of aerosol on winter and autumn Haze Days. The concentrations of WSIs in fine size rose sharply on Haze Days, leading the visibility to exponential decline. Differently, the concentrations of WSIs in coarse size were not the main cause in the change of the visibility.

Ambient volatile organic compounds (VOCs) were continuously measured during the high ozone (O3) periods from May 1 to May 31 and June 1 to July 16, 2015 at an industrial area in the north suburb of Nanjing. A positive matrix factorization (PMF) model and an observation-based model (OBM) were combined for the first time to investigate the contributions of VOC sources and species to local photochemical O3 formation. The average VOC concentrations in 2014 and 2015 were (36.47±33.44)×10-9 and (34.69±34.08)×10-9, respectively. The VOC sources identified by the PMF model for 2014 and 2015 belonged to 7 source categories, including vehicular emissions, liquefied petroleum gas usage, biogenic emissions, furniture manufacturing industry, chemical industry, chemical coating industry, and chemical materials industry emission sources. The OBM was modified to assess the O3 precursors' relationships. Generally, photochemical O3 production was VOC limited, with positive relative incremental reactivity (RIR) values for VOC species and a negative RIR value for NO. It can be seen that alkenes (1.20-1.79) and aromatics (1.42-1.48) presented higher RIR values and controlling O3 would be the most effective when the VOC emissions from alkenes were reduced by 80%. Vehicle emissions (1.01-1.11), LPG (0.74-0.82), biogenic emissions (0.34-0.42), and furniture manufacturing industry (0.32-0.49) sources were the top four VOC sources making significant contributions to photochemical O3 formation, which suggests that controlling vehicle emissions, biogenic emissions, LPG, and furniture manufacturing industry sources should be the most effective strategy to reduce photochemical O3 formation.

The data of ozone (O3) and its precursors (NOx, CO, VOCs) observed at northern suburb of Nanjing from December 01, 2013 to November 30, 2014 were used to analyze the difference of pollutant concentrations on weekends and weekdays, and its causes. The results showed that there was an obvious "Weekend Effect" in northern suburb of Nanjing. The mass concentrations of O3 on weekdays were higher than those on weekends, whereas mass concentrations of its precursors were higher on the weekends; The average mass concentrations of O3 were 19.84 μg·m-3, 53.45 μg·m-3, 57.17 μg·m-3, and 40.43 μg·m-3 in winter, spring, summer, and autumn respectively. Compared with other seasons, "Weekend Effect " was more distinct in spring. The value of NO2/NO was 4.81% higher on weekdays (3.63) than on weekends (3.46). The longer cumulative time and higher accumulation rate of O3, and the stronger atmospheric oxidation capacity on weekdays were responsible for the higher O3 mass concentrations on weekdays. The correlation coefficients of the mass concentrations of O3 with VOCs, NOx, NO, and NO2 were higher on weekdays than on weekends.

The particles number concentrations were determined by Wide-range Particle Spectrometer (WPS) in northern suburb of Nanjing in January and April 2015. The information of size distributions was applied in the multiple-path particle dosimetry model (MPPD) v.3.04 to quantify deposition fractions (DF) and number concentration (NC) depositions of fine particles in different regions of human airway, at different air quality levels, at rest and exercise. DF of nucleation mode and Aitken mode at rest and exercise were similar, while DF of accumulation mode at exercise was 2.49 times of that at rest. DF of nucleation mode and Aitken mode in pulmonary (PUL) was the highest, about 48.17% of total deposition fractions (TDF) at rest and 54.23% of TDF at exercise. DF of accumulation mode in head was the highest, about 41.23% of TDF at rest and 80.47% of TDF at exercise. The particle NC deposition in human airway in winter was lower than that in spring, and the total NC deposition in 3 regions was in the order of PUL > tracheobronchial(TB) > head. Compared with resting, nucleation mode deposition in PUL and accumulation mode deposition in TB and head increased at exercise. The worse the air quality, the higher the deposition growth rate of exercising to resting in head. DF difference among regions was mainly due to the different physiological parameters, while NC deposition difference was mainly due to the different particle NC in the local environment.

Volatile organic compounds (VOCs) were collected continuously during June–August 2013 and December 2013–February 2014 at an urban site in Nanjing in the Yangtze River Delta. The positive matrix factorization receptor model was used to analyse the sources of VOCs in different seasons. Eight and seven sources were identified in summer and winter, respectively. In summer and winter, the dominant sources of VOCs were vehicular emissions, liquefied petroleum gas/natural gas (LPG/NG) usage, solvent usage, biomass/biofuel burning, and industrial production. In summer, vehicular emissions made the most significant contribution to ambient VOCs (38%), followed by LPG/NG usage (20%), solvent usage (19%), biomass/biofuel burning (13%), and industrial production (10%). In winter, LPG/NG usage accounted for 36% of ambient VOCs, whereas vehicular emissions, biomass/biofuel burning, industrial production and solvent usage contributed 30, 18, 9, and 6%, respectively. The contribution of LPG/NG usage in winter was approximately four times that in summer, whereas the contribution from biomass/biofuel burning in winter was more than twice that in summer. The sources related to vehicular emissions and LPG/NG usages were important. Using conditional probability function analysis, the VOC sources were mainly associated with easterly, northeasterly and southeasterly directions, pointing towards the major expressway and industrial area. Using the propylene-equivalent method, paint and varnish (23%) was the highest source of VOCs in summer and biomass/biofuel burning (36%) in winter. Using the ozone formation potential method, the most important source was biomass/biofuel burning (32% in summer and 47% in winter). The result suggests that the biomass/biofuel burning and paint and varnish play important roles in controlling ozone chemical formation in Nanjing.

Absteact: BTEX concentrations were determined by GC5000 online gas chromatography in the atmosphere of the north suburb of Nanjing in March 2013 to February 2014, using the EPA human exposure analysis evaluation method for benzene series compounds of volatile organic compounds (ⅤOCs) in health risk assessment. The results showed that the total amount of BTEX showed the variation characteristics of spring ﹥ winter ﹥ autumn ﹥ summer. BTEX concentration was higher in the periods of 07:00- 10:00 and 17:00-20:00, and the lowest was detected between 13:00-15:00; At the weekend, the concentration of BTEX was higher than on the working day. The sources of BTEX included traffic sources, industrial sources and solvent evaporation. The HQ of BTEX in all four seasons showed the order of benzene ﹥ xylene ﹥ ethylbenzene ﹥ toluene, and the HQ risk values were within the safety range in all analysis periods. The distribution of R value was winter ﹥ autumn ﹥ spring ﹥ summer, and R was higher than the safety threshold for all the analyses, indicating the existence of carcinogenic risk.

BTEX concentrations were determined by GC5000 online gas chromatography in the atmosphere of the north suburb of Nanjing in March 2013 to February 2014, using the EPA human exposure analysis evaluation method for benzene series compounds of volatile organic compounds (VOCs) in health risk assessment. The results showed that the total amount of BTEX showed the variation characteristics of spring > winter > autumn > summer. BTEX concentration was higher in the periods of 07:00-10:00 and 17:00-20:00, and the lowest was detected between 13:00-15:00; At the weekend, the concentration of BTEX was higher than on the working day. The sources of BTEX included traffic sources, industrial sources and solvent evaporation. The HQ of BTEX in all four seasons showed the order of benzene > xylene > ethylbenzene > toluene, and the HQ risk values were within the safety range in all analysis periods. The distribution of R value was winter > autumn > spring > summer, and R was higher than the safety threshold for all the analyses, indicating the existence of carcinogenic risk.

Volatile organic compounds (VOCs) in the atmosphere of the north suburb of Nanjing in December 2015 were determined by GC5000 online gas chromatography,and the main composition and characteristics of VOCs were analyzed by using the PMF receptor model sources of VOCs parsing.The United States Environmental Protection Agency (EPA) human exposure analysis and evaluation method in the United States were used to evaluate Human health risk of benzene series.The results showed that there were 6 sources in the PMF mode.Natural gas leakage accounted for 32.05%,automobile exhaust accounted for 18.99%,solvent use 13.67%,industrial emissions 2 13.20%,gasoline volatile 11.72%,and industrial emissions 1(chemical type)10.36%.The high value areas of the emission source were in accordance with the location of pollution sources surrounding the observation point.The B/T ratio was 0.74,which was at a relatively high level.The noncarcinogenic risk hazard quotient value HQ at 06:00 reached the highest value.HQ risk values were within the safe range specified by EPA.HQ of each source was as follows:automobile exhaust emissions 20.67×10-2,solvent use 6.97×10-2,natural gas leakage 6.34×10-2.In the carcinogenic risk of benzene,automobile exhaust emissions was 4.11×10-6,and natural gas leakage was 1.09×10-6,both were higher than the EPA specified safety threshold.

Based on the data collected from May 15th to August 31st 2013 in an industrial area of Nanjing (a representative industrial area in the Yangtze River delta),characteristics of ozone (O3),PM2.5 and aerosol optical depth (AOD),and the relationships between O3 and PM2.5 and between O3 and AOD were analyzed.The effect of AOD on ozone formation was evaluated by the application of a detailed chemical mechanism model (NCAR MM).The average concentration of PM2.5 was 56.2±20.1 μg m-3,and the average AOD (500 nm) and Angstrom exponent α (440-870 nm) were 1.4± 0.9 and 1.0± 0.3,respectively.PM2.5 and O3 exceeded NAAQS-Ⅱ (the National Ambient Air Quality Standard Ⅱ) by 20.2％ and 10.1％,respectively.When PM2.5 exceeded the NAAQS-Ⅱ,the average AOD (500 nm) and α (440-870 nm) increased by 14.7％ and 23.91％,respectively,and O3 fell by 12.3％.When O3 exceeded the NAAQS-Ⅱ,the average AOD (500 nm) increased by 34.9％,and the average α (440-870 nm) did not vary significantly.There existed a significant linear correlation between daily ozone maximum concentration (y) and PM2.5 concentration (x) under the condition of high temperature and low relative humidity.When the relative humidity was less than 60％,the linear regression function was y=0.97x+43.96 [R2=0.60 (R2denotes the degree of fitting)].When the temperature was over 32℃,the linear regression function was y=1.24x+30.61 (R2=0.64).There existed a negative correlation between daily ozone maximum concentration (y) and ground-observed AOD (x) in general.There existed a good negative correlation between simulated daily ozone maximum concentration (y) and ground-observed AOD (x),and the regression functions could be written as v=-34.28x+ 181.62 (R2=0.93) and/or y=220.62.exp(-x/3.17)-19.50 (R2=0.99).

Volatile organic compounds (VOCs) were continuously observated in a northern suburb of Nanjing, a typical industrial area in the Yangtze River Delta, in a summer observation period from 15th May to 31st August 2013. The average concentration of total VOCs was (34.40±25.20)ppbv, including alkanes (14.98±12.72)ppbv, alkenes (7.35±5.93)ppbv, aromatics (9.06±6.64)ppbv and alkynes (3.02±2.01)ppbv, respectively. Source apportionment via Positive Matrix Factorization was conducted, and six major sources of VOCs were identified. The industry-related sources, including industrial emissions and industrial solvent usage, occupied the highest proportion, accounting for about 51.26% of the VOCs. Vehicular emissions occupied the second highest proportion, accounting for about 34.08%. The rest accounted for about 14.66%, including vegetation emission and liquefied petroleum gas/natural gas usage. Contributions of VOCs to photochemical O3 formation were evaluated by the application of a detailed chemical mechanism model (NCAR MM). Alkenes were the dominant contributors to the O3 photochemical production, followed by aromatics and alkanes. Alkynes had a very small impact on photochemical O3 formation. Based on the outcomes of the source apportionment, a sensitivity analysis of relative O3 reduction efficiency (RORE), under different source removal regimes such as using the reduction of VOCs from 10% to 100% as input, was conducted. The RORE was the highest (~20%–40%) when the VOCs from solvent-related sources decreased by 40%. The highest RORE values for vegetation emissions, industrial emissions, vehicle exhaust, and LPG/NG usage were presented in the scenarios of 50%, 80%, 40% and 40%, respectively.

Rapid economic growth has given rise to a significant increase in ozone (O3)-precursor emissions in many regions of China. An improved understanding of O3 formation in response to different precursor emissions is imperative to address the highly nonlinear O3 problem and to provide a solid scientific basis for efficient O3 abatement in these regions. To this end, this study was performed in Nanjing using a set of observational data from June 1, 2013, to May 31, 2014. The results showed that O3 concentrations were positively correlated with wind speed and temperature and were significantly negatively correlated with relative humidity. The highest monthly daytime, nighttime, and daily average O3 concentrations were observed in summer with values of approximately 46, 18 and 30 ppb, respectively. The lowest O3 concentrations were observed in November through January with values as low as 17, 4, and 9 ppb for the daytime, nighttime, and daily concentrations, respectively. The highest daytime average NO and NO2 concentrations were observed in December, whereas the lowest concentrations were observed in July. A unimodal O3 peak was observed with the highest O3 levels in summer followed by spring and then autumn; the lowest levels observed in the winter. The O3 concentration reached maximum levels at 14:00 to 15:00 h (local standard time). It was found that the crossover occurred with approximately several hours difference with the earliest occurring in summer (06:00 h) followed by spring (08:00 h), autumn (09:00 h), and winter (10:30 h). Furthermore, the highest constant rate of O3 accumulation was observed in summer (5.6 ppb/h) followed by autumn (4.8 ppb/h), spring (4.5 ppb/h), and winter (2.7 ppb/h). The oxidant intercept ranged from 28.4 ppb in January to 58.6 ppb in June, although the slope also shows substantial variation from 0.18 in June to 0.67 in August. The weekend effect is stronger in spring and summer than in autumn and winter and is more intense on Sundays than on Saturdays. Thus, the decrease of O3 levels during weekends suggests that it may be NO x -sensitive.