Taiwan Air Quality Research Articles

Climate change-induced impacts on PM2.5 in Taiwan under 2 and 4 °C global warming

Climate change can perturb meteorological parameters and affect air quality, human health, and the economy. This study examined the climate change-induced meteorological impacts on PM2.5 in Taiwan, an island with a dense population and complex topography. We conducted high-resolution simulations of the current and future climate using the National Center for Atmospheric Research's Weather Research and Forecasting model and the Community Multiscale Air Quality Modeling System. As verified by observations, the model could reproduce the spatial and seasonal characteristics of PM2.5 in Taiwan, including higher concentrations in cold seasons and lower concentrations in warm seasons. The number of polluted days in the current climate was more than 30 days per season in central Taiwan and more than 70 days in southern Taiwan in autumn and winter. Under the 2 °C and 4 °C global warming scenarios, climate change-induced impacts worsened the PM2.5 air quality in Taiwan in cold seasons. In winter, the number of polluted days increased by 3–6 days in the whole of Taiwan, while it decreased by 3–5 days in southern Taiwan in autumn. Analysis of climate change-induced impacts on northeasterly days, air stagnation, and ventilation indices revealed that the weaker low-level wind in cold seasons was the dominant factor of the worsened PM2.5 air quality under global warming in Taiwan. In addition, the climate change-induced worsening of PM2.5 air quality could be locally mitigated by northeasterly days and nighttime warming in urban areas in northern Taiwan and by precipitation in southern Taiwan. Overall, these findings indicated that climate change-induced impacts on air quality cover both urban and global scales. To better adapt to climate change, discussing interactions between the responses to global warming in urban areas using hierarchical models and high-resolution simulations is necessary.

The association between ambient air pollution exposure and connective tissue sarcoma risk: a nested case-control study using a nationwide population-based database.

A nationwide population-based database was utilized in a nested case-control study to explore the association between ambient air pollution exposure and the likelihood of developing connective tissue sarcoma. The study examined 280 cases of connective tissue sarcoma diagnosed between 2000 and 2012. A random sample of 1120 control subjects was selected from a subpopulation of claim records without a connective tissue sarcoma diagnosis in a 1:4 ratio. The control subjects were selected based on similar characteristics as the connective tissue sarcoma patients, including gender, birth year, and the year of diagnosis of the case group with medical records. Risk factors for connective tissue sarcoma were collected for analysis. Our data on exposure to air pollutants was collected from Taiwan's Air Quality Monitoring Network, which has been gathering air quality data from a growing network of sampling stations (now 76) throughout the country since 1997. It was discovered that the risk of connective tissue sarcoma was significantly increased by the Charlson comorbidity index (CCI), elevated levels of specific air pollution indices (e.g., total hydrocarbons (THC), fine particulate matter (PM2.5), and O3_8 (the annual mean of the daily maximum 8-h average concentration of O3), the High Pollutant Standards Index (hPSI) (the percentage of days in a given year in Taiwan where the PSI exceeds 100), and an insurable monthly wage over US$1100. Further investigation is needed to explore the involvement of these air pollutants in the formation of connective tissue sarcoma.

Long run renewable energy productivity, carbon capture patents and air quality in Taiwan

This study investigates the connection between nonrenewable energy productivity, renewable energy productivity, and air quality degradation in Taiwan from 2002 to 2019. We specifically emphasize the novel contribution of analyzing the productivity of renewable energy consumption. Robust estimation models, namely Nonlinear Autoregressive Distributed Lag (NARDL) and Robust Standard Estimation, are employed for comprehensive analyses. Our findings reveal a strong correlation between nonrenewable energy productivity and increased air pollutants, highlighting the significant impact of fossil fuels on air quality deterioration. Although renewable energy productivity demonstrates a negative association with air degradation, its effect is not statistically significant. This can be attributed to Taiwan's continued reliance on non-renewable energy sources within the overall energy mix. Hence, reducing dependence on fossil fuels is crucial for improving air quality. Importantly, the identified relationships have long-term implications, underscoring the necessity of persistent policy measures that promote renewable energy transition and emissions reduction over time. Our research emphasizes the urgency of addressing fossil fuel dependency to mitigate air pollution and highlights the potential benefits of enhancing renewable energy efficiency to achieve cleaner and healthier environments.

Estimation the effect of accumulated long-range transported pollutants during a PM2.5 event in Taiwan

Fine particulate matter (PM2.5) pollution has attracted much attention in recent years due to its adverse impact on human health and the environment. This study examined a major PM2.5 event observed in Taiwan on March 02–03, 2022, using the Weather Research and Forecasting - Community Multiscale Air Quality (WRF-CMAQ) models. This period also coincided with an outage caused by the power failure of three power plants in Southern Taiwan on March 03, 2022, which created a unique situation to distinguish the impacts of long-range transported (LRT) pollutants and the operation of power plants on the air quality in Taiwan during the high-air-pollution event.Four numerical experiments, Baseline (real case), Replace (Baseline with power plants), No_LRT (Baseline without LRT), and No_0303_LRT (Baseline without LRT on March 03, 2022), were conducted with different initial emission scenarios. The simulation results revealed that the PM2.5 concentrations emitted from three power plants only counted a relatively small proportion in total, while the LRT pollutants were the key factor contributing to the high-concentration PM2.5 during the event. Besides, we figured out that the high PM2.5 concentrations on March 03, 2022 were dominated by the contribution of accumulated LRT. In addition, the results also suggested that the existence of a lee-side vortex, which is considered a potential key issue of the mesoscale system contributes to extreme air pollutant events in Taiwan, causing encouraging conditions for the upward diffusion and northward transport of the PM2.5 concentrations over the southern part of Taiwan, as a result, the distribution of pollution was modulated.

Air Conditioning Operation Strategies for Comfort and Indoor Air Quality in Taiwan’s Elementary Schools

The Executive Yuan in Taiwan plans to install air-conditioning (A/C) in all elementary schools within two years. However, besides the associated energy consumption and environmental issues, the use of A/C will inevitably result in the doors and windows of the classroom being closed, which will increase the accumulation of carbon dioxide (CO2) within the classroom. An excessive indoor CO2 concentration can result in reduced cognitive performance and an impaired learning efficiency. Therefore, the moderate introduction of external air into the classroom is essential to increase the air exchange rate (AER) and reduce the CO2 concentration level. Accordingly, the present study conducts a numerical investigation into the effects of various A/C operation strategies on the CO2 concentration within the classroom given different proportions of students remaining in the classroom during the recess. Overall, the results indicate that the optimal usage strategy is to operate the A/C over the full school day (08:00~15:50 p.m.) in conjunction with a mechanical ventilation system providing a fresh air exchange rate of 5 l/s for every person in the room. However, the use of a mechanical ventilation system inevitably incurs an additional hardware and energy consumption. Thus, an alternative recommendation is also proposed, in which the windows are opened and the air conditioner is turned off at every recess and during the lunchtime period. It is shown that the resulting CO2 concentration in the classroom is still consistent with the Taiwan Environmental Protection Administration (EPA) regulations and the thermal comfort of the students is achieved for more than three-quarters of the school day.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions.

The unprecedented outbreak of COVID-19 significantly improved the atmospheric environment for lockdown-imposed regions; however, scant evidence exists on its impacts on regions without lockdown. A novel research framework is proposed to evaluate the long-term monthly spatiotemporal impact of COVID-19 on Taiwan air quality through different statistical analyses, including geostatistical analysis, change detection analysis and identification of nonattainment pollutant occurrence between the average mean air pollutant concentrations from 2018–2019 and 2020, considering both meteorological and public transportation impacts. Contrary to lockdown-imposed regions, insignificant or worsened air quality conditions were observed at the beginning of COVID-19, but a delayed improvement occurred after April in Taiwan. The annual mean concentrations of PM10, PM2.5, SO2, NO2, CO and O3 in 2020 were reduced by 24%, 18%, 15%, 9.6%, 7.4% and 1.3%, respectively (relative to 2018–2019), and the overall occurrence frequency of nonattainment air pollutants declined by over 30%. Backward stepwise regression models for each air pollutant were successfully constructed utilizing 12 meteorological parameters (R2 > 0.8 except for SO2) to simulate the meteorological normalized business-as-usual concentration. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model simulated the fate of air pollutants (e.g., local emissions or transboundary pollution) for anomalous months. The changes in different public transportation usage volumes (e.g., roadway, railway, air, and waterway) moderately reduced air pollution, particularly CO and NO2. Reduced public transportation use had a more significant impact than meteorology on air quality improvement in Taiwan, highlighting the importance of proper public transportation management for air pollution control and paving a new path for sustainable air quality management even in the absence of a lockdown.

ESTIMATING DEMAND FOR GOOD CLIMATE AND AIR QUALITY IN TAIWAN

We measure the amenity values of climate and air quality in Taiwan using the two-step hedonic price method (HPM). Separate hedonic housing price equations, one for each of three metropolitan areas that represent respective housing markets in Northern, Central, and Southern Taiwan, are estimated. We find evidence of a significant impact of climate and air quality on housing prices. As expected, residents in Taiwan prefer a warmer winter, cooler summer, lower humidity, and better air quality. Based on the results of the housing price regression, we compute implicit prices for environmental amenities, and then pool observations from all three metropolitan areas to estimate the demand for environmental amenities. The estimated demand functions for environmental amenities are used to evaluate the benefits and losses from changes in climate and air quality in Taiwan. Our results show that, compared to the benefits of a higher temperature in January, the losses from the rise in temperature in July are much higher in such a way that global warming is resulting in significant net losses in Taiwan. The area-specific welfare analysis in this study will help in informing climate change policy formulation and decision-making in Taiwan.

Risk of ambulance services associated with ambient temperature, fine particulate and its constituents

Short-term adverse health effects of constituents of fine particles with aerodynamic diameters less than or equal to 2.5 μm (PM2.5) have been revealed. This study aimed to evaluate the real-time health outcome of ambulance services in association with ambient temperature and mass concentrations of total PM2.5 level and constituents in Kaohsiung City, an industrialized city with the worst air quality in Taiwan. Cumulative 6-day (lag0-5) relative risk (RR) and 95% confidence interval (CI) of daily ambulance services records of respiratory distress, coma and unconsciousness, chest pain, headaches/dizziness/vertigo/fainting/syncope, lying at public, and out-of-hospital cardiac arrest (OHCA) in association with ambient temperature and mass concentrations of total PM2.5 level and constituents (nitrate, sulfate, organic carbon (OC), and elemental carbon (EC)) from 2006 to 2010 were evaluated using a distributed lag non-linear model with quasi-Poisson function. Ambulance services of chest pain and OHCA were significantly associated with extreme high (30.8 °C) and low (18.2 °C) temperatures, with cumulative 6-day RRs ranging from 1.37 to 1.67 at the reference temperature of 24–25 °C. Daily total PM2.5 level had significant effects on ambulance services of lying at public and respiratory distress. After adjusting the cumulative 6-day effects of temperature and total PM2.5 level, RRs of ambulance services of lying at public associated with constituents at 90th percentile versus 25th percentile were 1.35 (95% CI: 1.08, 1.68) for sulfate and 1.20 (95% CI: 1.02, 1.41) for EC, while RR was 1.31 (95% CI: 1.09–1.58) for ambulance services of headache/dizziness/vertigo/fainting/syncope in association with OC at 90th percentile versus 25th percentile. Cause-specific ambulance services had various significant association with daily temperature, total PM2.5 level, and concentrations of constituents. Elemental carbon may have stronger associations with increased ambulance services than other constituents.

Forecasting Air Quality in Taiwan by Using Machine Learning

This study proposes a gradient-boosting-based machine learning approach for predicting the PM2.5 concentration in Taiwan. The proposed mechanism is evaluated on a large-scale database built by the Environmental Protection Administration, and Central Weather Bureau, Taiwan, which includes data from 77 air monitoring stations and 580 weather stations performing hourly measurements over 1 year. By learning from past records of PM2.5 and neighboring weather stations’ climatic information, the forecasting model works well for 24-h prediction at most air stations. This study also investigates the geographical and meteorological divergence for the forecasting results of seven regional monitoring areas. We also compare the prediction performance between Taiwan, Taipei, and London; analyze the impact of industrial pollution; and propose an enhanced version of the prediction model to improve the prediction accuracy. The results indicate that Taipei and London have similar prediction results because these two cities have similar topography (basin) and are financial centers without domestic pollution sources. The results also suggest that after considering industrial impacts by incorporating additional features from the Taichung and Thong-Siau power plants, the proposed method achieves significant improvement in the coefficient of determination (R2) from 0.58 to 0.71. Moreover, for Taichung City the root-mean-square error decreases from 8.56 for the conventional approach to 7.06 for the proposed method.

Analysis of interpolation algorithms for the missing values in IoT time series: a case of air quality in Taiwan

Missing values are common in the Internet of Things (IoT) environment for various reasons, including regular maintenance or malfunction. In time-series prediction in the IoT, missing values may have a relationship with the target labels, and their missing patterns result in informative missingness. Thus, missing values can be a barrier to achieving high accuracy of prediction and analysis in data mining in the IoT. Although several methods have been proposed to estimate values that are missing, few studies have investigated the comparison of interpolation methods using conventional and deep learning models. There has thus far been relatively little research into interpolation methods in the IoT environment. To address these problems, this paper presents the use of linear regression, support vector regression, artificial neural networks, and long short-term memory to make time-series predictions for missing values. Finally, a full comparison and analysis of interpolation methods are presented. We believe that these findings can be of value to future work in IoT applications.

Seasonal variations in emission rates and composition of terpenoids emitted from Chamaecyparis formosensis (Cupressaceae) of different ages

Chamaecyparis formosensis (Cupressaceae) is among the most precious endemic conifers in Taiwan. Field study was conducted on seasonal variations in emission rates and compositions of terpenoids from this tree species of two different ages. A total of 21 terpenoids were detected, of which there were 13 monoterpenoids (MTs), 4 sesquiterpenoids (STs), and 4 diterpenoids (DTs). MTs dominated the emissions in both saplings and adult trees and produced more than 80% of terpene emissions. Contrasting seasonal pattern between saplings and adult trees was found. Total actual emissions from saplings were higher in cold seasons (range, 64.40 ± 13.18 to 140.74 ± 18.90 ng g−1 h−1) than in warm seasons (range, 55.63 ± 15.84 to 63.48 ± 11.85 ng g−1 h−1). Photosynthetically active radiation (PAR) was found to be the most important factor affecting terpene emissions from saplings. On the contrary, higher emissions were found in warm seasons for adult trees (range, 101.49 ± 12.29 to 181.35 ± 80.15 ng g−1 h−1), and the emissions were mainly in response to temperature. Some compounds in C. formosensis of both ages (e.g., β-myrcene, α-terpinene, trans-β-ocimene, terpinen-4-ol, α-cedrene and trans-β-farnesene) showed comparably higher contents in cold seasons. Results presented here provide important fundamental information for better understanding of forest bathing and estimating air quality in Taiwan.

Large–scale seasonal control of air quality in Taiwan

By investigating data during 2004–2015, this study explored the dynamic mechanisms of seasonal control of air quality over southern and northern Taiwan. Overall, higher (lower) pollution frequencies were observed in dry (wet) stages. Analysis of seasonality further suggested that pollution over southern Taiwan exhibited a symmetric characteristic, and that tropical modulation was a driving factor, whereas pollution over northern Taiwan displayed an asymmetric seasonality and likely followed the asymmetric East Asian seasonality. With the retreat of summer monsoon, air quality in southern Taiwan entered a poor phase rapidly; by contrast, good air quality was observed over northern Taiwan, which was due to the gradual southward migration of the western North pacific (WNP) high. Detailed dynamic processes contributing to atmospheric conditions and large-scale circulation were further compared between pollution and nonpollution cases. During dry stages, the midlatitude perturbations observed as wavy patterns had downstream effects on the atmospheric condition in Taiwan. The downstream effect of the midlatitude wave activity on air pollution was obviously found in the Dry Northerly stage in northern Taiwan and the Dry Southerly stage in southern Taiwan. In contrast to poor air quality during dry stages, air pollution was rarely observed during wet stages. The strength and location of the WNP high, which can be involved in the formation of nearby tropical cyclones, may mainly influence air quality in Taiwan.

Various Sources of PM2.5 and their Impact on the Air Quality in Tainan City, Taiwan

ABSTRACT The recent deterioration of Taiwan's air quality is partly due to fine particulate matter (PM2.5). With several studies pointing out a direct link between PM2.5 and the global disease burden, plans are underway to reach the standard of an annual average PM2.5 concentration of 15 µg m–3 in Taiwan by 2020. Subsequently, environmental protection bureaus in all cities should assess PM2.5 emission sources and implement control strategies. This study focuses on analysis of PM2.5 sources within Tainan City in an effort establish the contribution of large-scale pollution sources within the city as well as those from neighboring counties and cities. During this study, the top nine largest emission sources in Tainan City were investigated: (1) the Chemical manufacturing industry, (2) the iron and steel industry, (3) the power industry, (4) manufacturing of coal-based products, (5) diesel vehicles, (6) two-stroke scooters, (7) catering, (8) construction/road dust, and (9) open burning. Three important pollution sources in the central region of Taiwan were investigated as well, including: (1) the Taichung Power Plant, (2) the Formosa Petrochemical Corporation (FPCC) Sixth Naphtha Cracking Industry, and (3) the Dragon Steel Company in Taichung City, Changhua County, and Yunlin County, respectively, all of which are located on the windward side of Tainan City. The Community Multiscale Air Quality Model (CMAQ) was used to simulate the impact of the mentioned sources on Tainan’s air quality. The results for the monthly contributions from the different sources averaged over a one year period indicated that diesel vehicles are the largest source, emitting up to 1.06 µg m–3, followed by the Taichung Power Plant, which had 0.87 µg m–3 the construction industry and road dust emissions, with 0.80 µg m–3, and with open burning of waste having the lowest contribution. These results can be applied to facilitate the development of follow-up air quality control strategies.

Urban Air Pollution in Taiwan before and after the Installation of a Mass Rapid Transit System.

Urbanization causes air pollution in metropolitan areas, coupled with meteorological factors that affect air quality. Although previous studies focused on the relationships of urbanization, air pollution, and climate change in Western countries, this study evaluated long-term variations of air quality and meteorological factors in Taiwanese metropolitan areas (Taipei area, Taichung City, and Kaohsiung City) and a rural area (Hualien County) between 1993 and 2012. The influence of a mass rapid transit (MRT) system on air quality was also evaluated. Air pollutant concentrations and meteorology data were collected from Taiwan Environmental Protection Administration (TEPA) air monitoring stations and Central Weather Bureau stations in the surveyed areas, respectively. Analyses indicate that levels of air pollution in metropolitan areas were greater than in the rural area. Kaohsiung City had the highest levels of O, SO, and particulate matter 2.5 or 10 µm in diameter (PM and PM). Clear downward trends for CO, NO, PM, PM, and especially SO concentrations were found in the surveyed areas, whereas O showed no decrease. Both O and PM concentrations showed similar bimodal seasonal distributions. Taiwan's air quality has improved significantly since 1993, indicating the effectiveness of promoting air pollution strategies and policies by the TEPA. Air pollution had an obvious improvement in Taipei area after the MRT system began operations in 1996. Because global climate may potentially affect urban air pollution in Taiwan, further study to clarify the mechanisms by which air pollution may affect human health and other biological effects is warranted.

Potential impacts of electric vehicles on air quality in Taiwan

The prospective impacts of electric vehicle (EV) penetration on the air quality in Taiwan were evaluated using an air quality model with the assumption of an ambitious replacement of current light-duty vehicles under different power generation scenarios. With full EV penetration (i.e., the replacement of all light-duty vehicles), CO, VOCs, NOx and PM2.5 emissions in Taiwan from a fleet of 20.6 million vehicles would be reduced by 1500, 165, 33.9 and 7.2Ggyr−1, respectively, while electric sector NOx and SO2 emissions would be increased by up to 20.3 and 12.9Ggyr−1, respectively, if the electricity to power EVs were provided by thermal power plants. The net impacts of these emission changes would be to reduce the annual mean surface concentrations of CO, VOCs, NOx and PM2.5 by about 260, 11.3, 3.3ppb and 2.1μgm−3, respectively, but to increase SO2 by 0.1ppb. Larger reductions tend to occur at time and place of higher ambient concentrations and during high pollution events. Greater benefits would clearly be attained if clean energy sources were fully encouraged. EV penetration would also reduce the mean peak-time surface O3 concentrations by up to 7ppb across Taiwan with the exception of the center of metropolitan Taipei where the concentration increased by <2ppb. Furthermore, full EV penetration would reduce annual days of O3 pollution episodes by ~40% and PM2.5 pollution episodes by 6–10%. Our findings offer important insights into the air quality impacts of EV and can provide useful information for potential mitigation actions.

Fine particulate matter events associated with synoptic weather patterns, long-range transport paths and mixing height in the Taipei Basin, Taiwan

Asian dust storms (ADS) and PM2.5 (particle pollution) events have an evident influence on air quality in Taiwan. However, the synoptic weather patterns and atmospheric conditions on ADS days are not entirely similar to those related to PM2.5 event days. The aim of this study is to clarify the weather characteristics such as synoptic weather patterns, long-range transport paths, and stagnant conditions that precipitate PM2.5 events. Air quality and meteorological data from 2006 to 2013 were obtained from government-owned observation stations, and the mixing height was estimated in relation to the Nozaki planetary boundary layer height. This study used back trajectories as simulated gridded analysis data, which were based on kinematic trajectory analysis using NASA's GMAO (Global Modeling Assimilation Office) and NCEP (National Centers for Environmental Prediction) analyses. For testing the differences between means of two large, independent samples, the confidence interval of a common statistical indicator was employed. The results show that in comparison to low PM2.5 level days, weather features such as stagnant conditions, including low mixing height and low wind speed, low rainfall amount, and high solar hours, are favorable for inducing PM2.5 events. Eighty percent of the synoptic weather patterns on PM2.5 days were associated with either polar continental high pressure, a high-pressure system in mainland China moving from the continent to the sea, or a stationary front stretching from southern China to the East Sea, and moving eastwards. More than 81% of the contributing factors of the causes of PM2.5 events were found to be related to stagnant conditions. The pattern of the contributing factors causing the maximum-recorded concentration of PM2.5, (73.90 μg/m3) was attributed to local emissions, and a long-range transport time that was extended for a longer period over the land than over the sea. The synoptic weather patterns were also found to affect the spatial distribution of PM2.5 concentrations in the basin.

Impact of Long Transport on Taiwan’s Ozone Air Quality from East Asia

To clarify the influence of air pollutants emitted from East Asia on the ozone air quality in Taiwan, this study performs a long-term simulation result for 4 months using Taiwan Air Quality Model. Influence from the current (2007) and future (2020) East Asian emissions on the ozone concentration in Taiwan were simulated. The date ranges simulated were February, May, August, and October of 2007, representing the seasons of winter, spring, summer, and autumn. Influence from transboundary transport on Taiwan was assessed based on simulations of these 4 months. The influence of transboundary transport on the monthly average of daily peak ozone concentrations in Taiwan is 15.5 ppb. Worst case scenarios in 2020 will contribute an additional 3.7 ppb. If the size of ozone pollution area (≧120 ppb) is considered, transboundary transport contributed to 72 % of the polluted area in 2007; the ozone pollution area in the worst case scenario in 2020 will further increase by 47 % from 2007 levels.

Mining maximal frequent patterns from univariate uncertain data

In this paper, we propose mining maximal frequent patterns from univariate uncertain data. Univariate uncertain data refers to cases where each attribute in a transaction is associated with a quantitative interval and a probability density function that assigns a probability to each value in the interval. The number of frequent U2 patterns (i.e. frequent patterns of univariate uncertain data) is usually very large. To return a concise and informative mining result to users, we propose mining maximal frequent U2 patterns (MFU2Ps). A maximal frequent U2 pattern is a frequent U2 pattern without any frequent superset. The three proposed algorithms, MU2P-Miner, U2GenMax, and U2MAFIA, are different in terms of both the data formats used to store transactions and the structures used to store the MFU2Ps which are found during the mining process. The experiment results show that different algorithms excel when applied to different datasets and settings. We have applied the proposed algorithms to univariate uncertain data comprising measurements of the air quality and weather conditions in Taiwan; the derived MFU2Ps show that the air quality in Taiwan is usually good (unless a sand storm affects the island) and the weather is often wet.

A Study on the Use of a Statistical Analysis Model to Monitor Air Pollution Status in an Air Quality Total Quantity Control District

The air quality in Taiwan, at present, is determined by a pollution standard index (PSI) that is applied to areas of possible serious air pollution and Air Quality Total Quantity Control Districts (AQTQCD). Many studies, both in Taiwan and in other countries have examined the characteristics and levels of air pollution with PSI. This study uses air quality data collected from eight automatic air quality monitoring stations in an AQTQCD in central Taiwan and discusses the correlation between air quality variables with statistical analysis in an attempt to accurately reflect the difference of air quality observed by each monitoring station as well as to establish an air quality classification system suitable for the whole Taiwan. After using factor analysis (FA), seven air pollutants are grouped into three factors: organic, photochemical, and fuel. These three factors are the dominant ones in regards to the air quality of central Taiwan. Cluster analysis is used to classify air quality in central Taiwan into five clusters to present different characteristics and pollution degrees of air quality. This research results should serve as a reference for those involved in the review of air quality management effectiveness and/or the enactment of management control strategies.

Stream mining on univariate uncertain data

In this paper, we propose mining frequent patterns from univariate uncertain data streams, which have a quantitative interval for each attribute in a transaction and a probability density function indicating the possibilities that the values in the interval appear. Many data streams comprise flows of univariate uncertain data, for example, the records of atmospheric pollution sensors, and network monitoring records. We propose two algorithms to address this issue: the ExactU2Stream algorithm and the ApproxiU2Stream algorithm. The former incrementally stores the incoming transactions, and delays the mining process until it is requested. The latter mines the transactions immediately when they arrive, and stores the derived frequent patterns. Compared with the latter, the former returns results that are more accurate, but it also requires more response time. Both algorithms utilize the sliding window scheme, which decomposes the continuous data stream into discrete, overlapping chunks. The proposed algorithms outperform the compared methods in terms of runtime and memory usage. We have applied the two proposed algorithms to the data streams recording the air quality in Taiwan; the derived frequent patterns not only show the common air quality in Taiwan but also show the extremely bad air quality when a sand storm affects Taiwan.