Trends in concentration of ground-level ozone and meteorological conditions during high ozone episodes in the Kao-Ping Airshed, Taiwan

Deseasonalized trend of ground-level ozone and its precursors in an industrial city Kaohsiung, Taiwan

Mitigating ground-level ozone (GLO) remains challenging due to its highly nonlinear formation process. Thus, understanding GLO pollution trends is crucial for developing effective control strategies, especially Kaohsiung industrial city, Taiwan. Based on the long-term monitoring data set of 2011-2022, temporal analysis reveals that monthly mean GLO peaks in autumn (40.66 ± 5.10 ppb), carbon monoxide (CO) and major precursors such as nitrogen oxides (NOx), nonmethane hydrocarbons (NMHC) reach their highest levels in winter. The distinct seasonal variation of air pollutants in Kaohsiung is primarily influenced by the unique blocking effect of the mountainous area under the northeasterly wind, as the city is situated downwind, causing high GLO levels during autumn due to the accumulation of stagnant air hindering the dispersion of pollutants. Over the 12 years (2011-2022), the deseasonalized trend analysis was conducted with p < 0.001, revealing a stabilization trend of GLO (+0.04 ppb/yr) from a previous sharp increase. The observed improvement is credited to a drastic decrease in total oxidants (Ox) at -0.63 ppb/yr due to significantly reducing their precursors. Furthermore, the effectiveness of precursor reduction is also supported by GLO daily maximum profile changes. While high GLO events (>120 ppb) decrease, days within midrange (60-80 ppb) rise from 24.4% to 33.3%. A notable difference emerges when comparing daytime and nighttime GLO. While daytime GLO decreased at -0.22 ppb/yr, nighttime GLO increased at +0.34 ppb/yr. Weakened nocturnal titration effects accounted for the nighttime increase. The distinct spatial variations in GLO trends on a citywide scale underscore that areas with complicated industrial activities may not benefit from a continuing reduction of precursors compared to less-polluted areas. The findings of this study hold significant implications for improving GLO control strategies in heavily industrialized city and provide valuable information to the general public about the current state of GLO pollution.

Greenspace pattern, meteorology and air pollutant in Taiwan: A multifaceted connection

Air pollution is a global environmental concern that poses a significant threat to human health. Given the impact of urbanization and climate change, green planning is being encouraged to improve air quality. The study aims to examine the intricate relationships between greenspace pattern and outdoor air around 73 in-situ stations over Taiwan during the dry (November to April) and wet (May to December) seasons from 2015 to 2020. To achieve this, Partial Least Squares - Structural Equation Modeling was utilized to analyze the interactions among seven dimensions: greenspace - GS, gaseous pollutant - GP, particle pollutant - PP, O3 - OZONE, air temperature - TEMP, relative humidity - RH, and wind speed - WS. The GS involves seven landscape metrics: edge density, total edge, effective mesh size, largest patch area, percentage of landscape, total core area, and patch cohesion index. The results indicate that the GS has a stronger effect on the GP, whereas its effect on the PP is weaker during the dry season compared to the wet season. While its effect on the TEMP is weaker, it shows a slightly stronger effect on the RH during the dry season. Moreover, the GS mediates the air pollutant dimensions during the two seasons, with the RH acting as a primary mediator. The meteorological dimensions primarily have a greater influence on the air pollutant dimensions during the dry season than the wet season. Consequently, the GS explains 11.3 % more and 18.4 % less of the variances in the RH and TEMP during the dry season, respectively. Moreover, the GS and meteorological dimensions yield a seasonal difference in explained variance, with the highest value observed for the OZONE (R2 = 24.2 %), followed by the PP (R2 = 9.7 %) and GP (R2 = 7.7 %). Notably, seven landscape metrics serve as potential indicators for green strategies in urban planning to enhance outdoor air quality.

Fifty-year change in air pollution in Kaohsiung, Taiwan

The change in air quality in cities can be the product of regulation and emissions. Regulations require enforcement of emission reduction, but it is often shifting economic and societal structures that influence pollutant emissions. This study examines the long-term record of air pollutants in Kaohsiung, where post-war industrialisation increased pollution substantially, although improvements are observed in recent decades as the city moved to a more mixed economy. The study tracks both gases and particles across a period of significant change in pollution sources in the city. Concentrations of SO₂ and aerosol SO₄^2- were especially high ~1970, but these gradually declined, although SO₄^2- to a lesser extent than its precursor, SO₂. While twenty-first century emissions of SO₂ and NO_x have declined, this has been less so for NH₃, because it arises from predominantly agricultural sources. The atmosphere in Kaohsiung continues to have high concentrations of O₃, and these have risen in the city, likely a product of less titration by NO. The changes have meant that ozone has become an increasing threat to health and agriculture. Despite a potential for producing (NH₄)₂SO₄ and NH₄NO₃ aerosols, a product of a relatively constant supply of NH₃, visibility has improved in recent years. Emissions of SO₂ and NO_x should continue to be reduced, as these strongly affect the amount of fine secondary aerosol. However, the key problem may be ozone, which is difficult to control as it requires careful consideration of the balance of NO_x and hydrocarbons so important to its production.

How a winding-down oil refinery park impacts air quality nearby?

It is always difficult to compare, let alone estimate, the difference of air pollutant concentrations before and after closure of a major source because the pollutants cannot be traced or predicted after entering the ambient. Indeed, we are not aware of any studies specifically related to the air pollutants impacted by a winding-down source. In this work, we applied nine years (2010-2018) online measurement of air pollutants (including PM₁₀, PM_2.5, NO₂, SO₂, O₃ and VOCs) to investigate (i) the temporal behavior of air pollutants before and after closure of an oil refinery park by using pair-wise statistics and correlations between wind speed and direction, and (ii) the source impacts on O₃ concentrations using PMF coupled with multiple linear regression (MLR) analysis (PMF-MLR). Example applications are presented at two monitoring sites (A and B) close to the Kaohsiung Oil Refinery (KOR), located in the southern industrial city of Taiwan. The results show that the KOR shutdown changed air pollutant concentrations to a certain extent in these study areas. We also conclude that, instead of using propylene-equivalent and ozone formation potential (OFP) concentrations, it is better to estimate the formation of O₃ based on PMF-MLR analysis as developed in this study. The PMF analysis has identified various VOCs sources at both sites including solvent usage, petrochemical industrial sources, industrial emissions, vehicle-related sources, vegetation emissions and aged air-masses. Also, the MLR model shows that both the background sources and petrochemical industrial sources may significantly change O₃ concentrations.

A mixed spatial prediction model in estimating spatiotemporal variations in benzene concentrations in Taiwan

It is well known benzene negatively impacts human health. This study is the first to predict spatial-temporal variations in benzene concentrations for the entirety of Taiwan by using a mixed spatial prediction model integrating multiple machine learning algorithms and predictor variables selected by Land-use Regression (LUR). Monthly benzene concentrations from 2003 to 2019 were utilized for model development, and monthly benzene concentration data from 2020, as well as mobile monitoring vehicle data from 2009 to 2019, served as external data for verifying model reliability. Benzene concentrations were estimated by running six LUR-based machine learning algorithms; these algorithms, which include random forest (RF), deep neural network (DNN), gradient boosting (GBoost), light gradient boosting (LightGBM), CatBoost, extreme gradient boosting (XGBoost), and ensemble algorithms (a combination of the three best performing models), can capture how nonlinear observations and predictions are related. The results indicated conventional LUR captured 79% of the variability in benzene concentrations. Notably, the LUR with ensemble algorithm (GBoost, CatBoost, and XGBoost) surpassed all other integrated methods, increasing the explanatory power to 92%. This study establishes the value of the proposed ensemble-based model for estimating spatiotemporal variation in benzene exposure.

Comprehensive analysis of ambient air quality during second lockdown in national capital territory of Delhi

The lockdown imposed in Delhi, due to the second wave of the COVID-19 pandemic has led to significant gains in air quality. Under the lockdown, restrictions were imposed on movement of people, operation of industrial establishments and hospitality sector amongst others. In the study, Air Quality Index and concentration trends of six pollutants, i.e. PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ were analysed for National Capital Territory of Delhi, India for three periods in 2021 (pre-lockdown: 15 March to 16 April 2021, lockdown: 17 April to 31 May 2021 and post-lockdown: 01 June to 30 June). Data for corresponding periods in 2018-2020 was also analysed. Lockdown period saw 6 days in satisfactory AQI category as against 0 days in the same category during the pre-lockdown period. Average PM_2.5, PM₁₀, NO₂ and SO₂ concentrations reduced by 22%, 31%, 25% and 28% respectively during lockdown phase as compared to pre-lockdown phase, while O₃ was seen to increase. Variation in meteorological parameters and correlation of pollutants has also been examined. The significant improvement arising due to curtailment of certain activities in the lockdown period indicates the importance of local emission control, and helps improve the understanding of the dynamics of air pollution, thus highlighting policy areas to regulatory bodies for effective control of air pollution.

Using a land use regression model with machine learning to estimate ground level PM2.5

Ambient fine particulate matter (PM_2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM_2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM_2.5. Daily average PM_2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM_2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM_2.5 exposures.

Comparison of air pollutant-related hospitalization burden from AECOPD in Shijiazhuang, China, between heating and non-heating season

Few researches have been investigated on the effects of ambient air pollutants from coal combustion on acute exacerbation of chronic obstructive pulmonary disease (AECOPD) hospitalizations. The whole time series was split into heating season and non-heating season. We used a quasi-Poisson generalized linear regression model combined with distributed lag non-linear models (DLNMs) to estimate the relative cumulative risk and calculate the air pollutant hospitalization burden of AECOPD for lag 0-7 days in heating season and non-heating season. There were higher PM_2.5, PM₁₀, NO₂, SO₂, and CO concentrations in heating seasons than non-heating season in Shijiazhuang; however, O₃ was higher in non-heating season than heating season. The AECOPD-associated relative cumulative risks for PM_2.5, PM₁₀, NO₂, and SO₂ for lag 0-7 days were significantly positively associated with hospitalization in heating and non-heating season; we found that the cumulative relative risk of NO₂ was the greatest in every 1 unit of air pollutants during the heating season and the cumulative relative risk of SO₂ was the greatest during the non-heating season. The results showed that 17.8%, 12.9%, 1.7%, 16.7%, and 10.5% of AECOPD hospitalizations could be attributable to PM_2.5, PM₁₀, SO₂, NO₂, and CO exposure in heating season, respectively. However, the results showed that 19.5%, 22.4%, 15%, 8.3%, and 10.4% of AECOPD hospitalizations could be attributable to PM_2.5, PM₁₀, SO₂, NO_2, and O₃ exposure in non-heating season, respectively. The attributable burden of AECOPD hospitalization in heating season and non-heating season are different. PM_2.5, PM₁₀, NO₂, and CO are the main factors of heating season, while PM₁₀, PM_2.5, SO₂, and O₃ are the main factors of non-heating season. In conclusions, the centralized heating can change the influence of attributable risk. When government departments formulate interventions to reduce the risk of acute hospitalization of chronic obstructive pulmonary disease (COPD), the influence of heating on disease burden should be considered.

Long-term (2005-2015) trend analysis of PM2.5 precursor gas NO2 and SO2 concentrations in Taiwan

Ground air monitoring stations have been installed in Taiwan since 1993 to ensure whether the criteria air pollutants meet the ambient air quality standards. In the present study, the data from the monitoring stations were used to evaluate long-term (2005-2015) trend of NO₂ and SO₂ in three metropolitan cities (northern Taipei, central Taichung, and southern Kaohsiung), two eastern coastal cities (Hualien and Taitung), and one agricultural city in west-central plain (Douliu); those cities essentially covered the entire region of Taiwan. The results indicate that SO₂ and NO₂ concentrations of all studied six cities meet the annual average standards of 30 and 50 ppb, respectively. After deseasonalizing the original data and using 7-month moving average, the trend analysis reveals a decreasing trend ranging from 0.15 to 0.57 ppb/year (R² from 0.33 to 0.85) for NO₂ and 0.06 to 0.45 ppb/year (R² from 0.32 to 0.92) for SO₂; the corresponding reductions over the 10-year span are 4 to 42% for NO₂ and 22 to 52% for SO₂. The reduction trend, despite the growth in GDP, vehicle numbers and energy consumption, industrial output, etc., is similar to those of developed countries. Clearly, there are seasonal/monthly variation patterns for these two precursor gases with minimum levels in summer (July) and maximum in winter (December). The concentration reductions, however, were lagging behind the respective emission reductions. There are significant correlations among six cites for NO₂ (r = 0.58-0.93) and, to some extent, SO₂ (0.32-0.66). The correlation between SO₂ and NO₂ (r = 0.46-0.74) indicates same or similar emission sources. Furthermore, the correlation between observed pollutant concentrations and their emission is excellent for SO₂ in two cities (0.79-0.96). The SO₂/NO₂ ratios vary with city and time and the value is site specific. For example, in 2005, the SO₂/NO₂ ratio was 0.38 in Kaohsiung and 0.18 in both Taipei and Taichung, the latter reflecting significant contribution from mobile sources. However, they all converged to 0.18-0.28 in 2015 in the six cities evaluated. All in all, the policies/measures made by the central and local government are effective in reducing ambient SO₂ and NO₂ levels.

Association between Atmospheric Fine Particulate Matter and Hospital Admissions for Chronic Obstructive Pulmonary Disease in Southwestern Taiwan: A Population-Based Study

Objectives: This paper reports on the findings of a population-based study to evaluate the relationship between atmospheric fine particulate matter (PM_2.5) levels and hospital admissions for chronic obstructive pulmonary disease (COPD) in southwestern Taiwan over a three-year period, 2008–2010. Methods: Data on hospital admissions for COPD and PM_2.5 levels were obtained from the National Health Insurance Research database (NHIRD) and the Environmental Protection Administration from 2008 to 2010, respectively. The lag structure of relative risks (RRs) of hospital admissions for COPD was estimated using a Poisson regression model. Results: During the study period, the overall average hospitalization rate of COPD and mean 24-h average level of PM_2.5 was 0.18% and 39.37 μg/m³, respectively. There were seasonal variations in PM_2.5 concentrations in southwestern Taiwan, with higher PM_2.5 concentrations in both spring (average: 48.54 μg/m³) and winter (49.96 μg/m³) than in summer (25.89 μg/m³) and autumn (33.37 μg/m³). Increased COPD admissions were significantly associated with PM_2.5 in both spring (February–April) and winter (October–January), with the relative risks (RRs) for every 10 μg/m³ increase in PM_2.5 being 1.25 (95% CI = 1.22–1.27) and 1.24 (95% CI = 1.23–1.26), respectively, at a lag zero days (i.e., no lag days). Lag effects on COPD admissions were observed for PM_2.5, with the elevated RRs beginning at lag zero days and larger RRs estimates tending to occur at longer lags (up to six days, i.e., lag 0–5 days). Conclusions: In general, findings reveal an association between atmospheric fine particulate matter (PM_2.5) and hospital admissions for COPD in southwestern Taiwan, especially during both spring and winter seasons.

Temporal characteristics of black carbon concentrations and its potential emission sources in a southern Taiwan industrial urban area

This study investigates the temporal characteristics of black carbon and its potential emission sources, as well as the fractions of BC in PM2.5 levels in Kaohsiung urban area, which is an industrial city in southern Taiwan. Concentrations of BC and PM2.5 are monitored continuously from March 2006 to February 2010, using an aethalometer and a tapered element oscillating microbalance monitor. Additionally, the presence of organic compounds (or UV enhanced species) in particles at the sampling site is determined using the Delta-C (UVBC-BC) value. According to long-term measurement results, BC and PM2.5 concentrations are 3.33 and 34.0 μg m(-3), respectively, in the Kaohsiung urban area. The ratio of BC/PM2.5 is approximately 11 %. Low concentration of BC and PM2.5 in the summer of this study period is mostly likely owing to meteorological conditions that favored dispersion of local air pollutants. Nevertheless, BC concentrations peaked markedly during morning hours (7:00-11:00), likely owing to local traffic congestion. Measurement results suggest that BC is released from local traffic activities and emitted from industrial activities at this sampling site. Additionally, Delta-C values are significantly higher than zero during January-March and November-December periods in this industrial urban area, implying that UV enhanced species can be observed. At this sampling site, these UV enhanced species do not only originate from household activity and solid waste burning but also release from industrial activities. The elevated Delta-C values during nighttime (18:00-6:00) in the autumn and winter seasons are likely related to those UV enhanced species in the atmosphere, which can be condensed on particle surface under low temperature conditions. According to long-term measurement results, significantly positive Delta-C values can be observed under temperatures <20 °C and relative humidity of 60-75 % in this study. Despite the household activity and solid waste burning, the major sources of particles that are bound with UV enhanced species in this sampling site are industrial parks and a coal-fired power plant.

Comparing ozone metrics on associations with outpatient visits for respiratory diseases in Taipei Metropolitan area

This study reported cumulative 6-day (lag 0-5 days) relative risks (RR) and confidence intervals (CI) of daily outpatient visits for total respiratory disease (RD), asthma, and chronic airway obstruction not otherwise classified (CAO) associated with three ozone metrics (daily 1-h maximum (O3, 1 h max), 8-h average maximum (O3, 8 h max), 24-h average (O3, 24 h avg)), and an alternative oxidant indicator (Ox) in Taipei Metropolitan, using distributed lag non-linear models after controlling for potential confounders. The Ox showed the strongest association with outpatient visits for total RD (RR = 1.10, 95% CI: 1.10, 1.11) and asthma (RR = 1.18, 95% CI: 1.00, 1.39) in the cold season. The O3, 24 h avg appeared to be the optimal ozone metric associating with total RD than O3, 1 h max and O3, 8 h max based on model selection. In conclusion, outpatient visits for total RD associated with ozone vary with ozone metrics, disease and season.

Applying model simulation and photochemical indicators to evaluate ozone sensitivity in southern Taiwan

Ozone sensitivity was investigated using CAMx simulations and photochemical indicator ratios at three sites (Pingtung City, Chao-Chou Town, and Kenting Town) in Pingtung County in southern Taiwan during 2003 and 2004. The CAMx simulations compared fairly well with the hourly concentrations of ozone. Simulation results also showed that Pingtung City was mainly a volatile organic compounds (VOC)-sensitive regime, while Chao-Chou Town was either a VOC-sensitive or a NOx-sensitive regime, depending on the seasons. Measurements of three photochemical indicators (H2O2, HNO3, and NOy) were conducted, and simulated three transition ranges of H2O2/HNO3 (0.5-0.8), O3/HNO3 (10.3-16.2) and O3/NOy (5.7-10.8) were adopted to assess the ozone sensitive regime at the three sites. The results indicated that the three transition ranges yield consistent results with CAMx simulations at most times at Pingtung City. However, both VOC-sensitive and NOx-sensitive regimes were important at the rural site Chao-Chou Town. Kenting Town, a touring site at the southern end of Taiwan, was predominated by a NOx-sensitive regime in four seasons.

Measurement and source characteristics of carbonyl compounds in the atmosphere in Kaohsiung city, Taiwan

The concentrations of eighteen atmospheric carbonyls species were measured by the LpDNPH-Cartridge and the microcomputer air sampling device at Nan-Chie (northern part) and Hsiung-Kong (southern part) sites in Kaohsiung city, southern Taiwan. These samples were then analyzed using a high performance liquid chromatography (HPLC). Measurements showed that the highest concentrations of carbonyls were formaldehyde (18.33 and 18.74 microg m(-3)) at the Nan-Chie and Hsiung-Kong site, followed by acetaldehyde (14.90 and 15.71 microg m(-3)). The concentrations of total carbonyls were higher at Hsiung-Kong site (66.96 microg m(-3)) than at Nan-Chie site (60.41 microg m(-3)). The concentrations of total carbonyls at Nan-Chie site (or Hsiung-Kong site) were 74.06 microg m(-3) (89.99 microg m(-3)) in summer and 37.14 microg m(-3) (46.50 microg m(-3)) in winter, due to the fact that photochemical activities are stronger in summer than in winter. The results of principal component analysis (PCA)/absolute principal component scores (APCS) suggest that the primary pollution sources at Nan-Chie were vehicle exhausts (gasoline and diesel engines), stationary emissions (petrochemical and food industry) and restaurant emissions, and the primary pollution sources at Hsiung-Kong were vehicle exhausts (gasoline and diesel engines), stationary emissions (metal assembly and petrochemical industry) and restaurant emissions.

Climate change and allergic disease

Climate change is potentially the largest global threat to human health ever encountered. The earth is warming, the warming is accelerating, and human actions are largely responsible. If current emissions and land use trends continue unchecked, the next generations will face more injury, disease, and death related to natural disasters and heat waves, higher rates of climate-related infections, and wide-spread malnutrition, as well as more allergic and air pollution-related morbidity and mortality. This review highlights links between global climate change and anticipated increases in prevalence and severity of asthma and related allergic disease mediated through worsening ambient air pollution and altered local and regional pollen production. The pattern of change will vary regionally depending on latitude, altitude, rainfall and storms, land-use patterns, urbanization, transportation, and energy production. The magnitude of climate change and related increases in allergic disease will be affected by how aggressively greenhouse gas mitigation strategies are pursued, but at best an average warming of 1 to 2 degrees C is certain this century. Thus, anticipation of a higher allergic disease burden will affect clinical practice as well as public health planning. A number of practical primary and secondary prevention strategies are suggested at the end of the review to assist in meeting this unprecedented public health challenge.

Outdoor air pollution and emergency department visits for asthma among children and adults: a case-crossover study in northern Alberta, Canada

BACKGROUND

Recent studies have observed positive associations between outdoor air pollution and emergency department (ED) visits for asthma. However, few have examined the possible confounding influence of aeroallergens, or reported findings among very young children.

METHODS

A time stratified case-crossover design was used to examine 57,912 ED asthma visits among individuals two years of age and older in the census metropolitan area of Edmonton, Canada between April 1, 1992 and March 31, 2002. Daily air pollution levels for the entire region were estimated from three fixed-site monitoring stations. Similarly, daily levels of aeroallergens were estimated using rotational impaction sampling methods for the period between 1996 and 2002. Odds ratios and their corresponding 95% confidence intervals were estimated using conditional logistic regression with adjustment for temperature, relative humidity and seasonal epidemics of viral related respiratory disease.

RESULTS

Positive associations for asthma visits with outdoor air pollution levels were observed between April and September, but were absent during the remainder of the year. Effects were strongest among young children. Namely, an increase in the interquartile range of the 5-day average for NO2 and CO levels between April and September was associated with a 50% and 48% increase, respectively, in the number of ED visits among children 2 - 4 years of age (p < 0.05). Strong associations were also observed with these pollutants among those 75 years of age and older. Ozone and particulate matter were also associated with asthma visits. Air pollution risk estimates were largely unchanged after adjustment for aeroallergen levels.

CONCLUSION

Our findings, taken together, suggest that exposure to ambient levels of air pollution is an important determinant of ED visits for asthma, particularly among young children and the elderly.

Meteorologically adjusted ground level ozone trends in southern Taiwan

Two methods were used to calculate the meteorologically adjusted ground level ozone trends in southern Taiwan. The first method utilized is a robust linear regression method. The second approach uses a multilayer perceptron (MLP) artificial neural network (ANN) method. The observations obtained from 16 monitoring stations were analyzed and divided into six groups by hierarchical divisive clustering procedure. The daily maximum 1 and 8 h ozone concentrations for each group are then calculated. The meteorologically adjusted trends obtained by linear regression and MLP methods are smaller than the unadjusted trends for all groups and average time. It indicts that the meteorological conditions in Taiwan tend to increase ambient ozone concentrations in recent years.