Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chemical species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chemical and physical properties of complex air pollutant mixtures, and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiological and toxicological research, and to better quantify the effects of specific source sectors and mitigation strategies.

A field study was conducted to determine the effects of ambient conditions and burning practices of rice fields in Taiwan on the chemical and physical characteristics of the smoke aerosol. Rice straw was burned on an actual rice field under typical conditions and smoke particles were collected immediately downwind of the field over the full particle size spectrum. Here we present size distributions of levoglucosan, a common molecular tracer for biomass burning, as well as detailed concentration patterns of three anhydrosugars, including mannosan, and galactosan, in addition to smoke aerosol concentrations of inorganic ions and carbonaceous species. The generated smoke aerosol was characterized by a high OC/EC ratio (10) and a large fraction of potassium (K⁺) and chloride (Cl⁻) ions at a Cl⁻/K⁺ ratio of 2. Levoglucosan showed a distinct bimodal distribution in the smoke particles with a large fraction (up to 56%) of the total levoglucosan mass observed in very large particles (PM _{> 10} ). The prevailing ambient conditions (such as relatively high humidity), atmospheric processes (e.g., particle coagulation, hygroscopic growth, and deposition), the specific burning practices of rice fields in Taiwan (slow burning of straw spread in thin layers on the ground), as well as the inherent properties of rice straw likely influenced the particle size characteristics of the smoke tracer. Moreover, the relative abundance of the three biomass burning tracers showed a unique pattern (in good agreement with previous chamber burn measurements): levoglucosan-to-mannosan ratios were distinctly higher (with an average value of 27) than those observed for other types of biomass, such as softwood, hard wood, peat, or leaves, in previous studies. Such chemical fingerprint may be used in source apportionment studies for the assessment of contributions from the combustion of specific types of biomass.

As the population in cities continues to increase rapidly, air pollution becomes a serious issue from public health to social economy. Among all pollutants, fine particulate matters (PM2.5) directly related to various serious health concerns, e.g., lung cancer, premature death, asthma, and cardiovascular and respiratory diseases. To enhance the quality of urban living, sensors are deployed to create smart cities. In this paper, we present a participatory urban sensing framework for PM2.5 monitoring with more than 2500 devices deployed in Taiwan and 29 other countries. It is one of the largest deployment project for PM2.5 monitor in the world as we know until May 2017. The key feature of the framework is its open system architecture, which is based on the principles of open hardware, open source software, and open data. To facilitate the deployment of the framework, we investigate the accuracy issue of low-cost particle sensors with a comprehensive set of comparison evaluations to identify the most reliable sensor. By working closely with government authorities, industry partners, and maker communities, we can construct an effective eco-system for participatory urban sensing of PM2.5 particles. Based on our deployment achievements to date, we provide a number of data services to improve environmental awareness, trigger on-demand responses, and assist future government policymaking. The proposed framework is highly scalable and sustainable with the potential to facilitate the Internet of Things, smart cities, and citizen science in the future.

The incidence of extreme precipitation has increased with the exacerbation of worldwide climate disruption. We hypothesize an association between precipitation and the distribution patterns that would affect the endemic burden of 8 infectious diseases in Taiwan, including water- and vector-borne infectious diseases. A database integrating daily precipitation and temperature, along with the infectious disease case registry for all 352 townships in the main island of Taiwan was analysed for the period from 1994 to 2008. Four precipitation levels, 350 mm, were categorized to represent quantitative differences, and their associations with each specific disease was investigated using the Generalized Additive Mixed Model and afterwards mapped on to the Geographical Information System. Daily precipitation levels were significantly correlated with all 8 mandatory-notified infectious diseases in Taiwan. For water-borne infections, extreme torrential precipitation (>350 mm/day) was found to result in the highest relative risk for bacillary dysentery and enterovirus infections when compared to ordinary rain (<130 mm/day). Yet, for vector-borne diseases, the relative risk of dengue fever and Japanese encephalitis increased with greater precipitation only up to 350 mm. Differential lag effects following precipitation were statistically associated with increased risk for contracting individual infectious diseases. This study’s findings can help health resource sector management better allocate medical resources and be better prepared to deal with infectious disease outbreaks following future extreme precipitation events.