University of Macau / Faculty of Science and Technology. / Department of Civil and Environmental Engineering

Chapter 1: We provide first evidence of a link from daily air pollution exposure to sleep loss in a panel of Chinese cities. We develop a social media-based, city-level metric for sleeplessness, and bolster causal claims by instrumenting for pollution with plausibly exogenous variations in wind patterns. Estimates of effect sizes are substantial and robust. In our preferred specification, a one standard deviation increase in AQI causes an 11.6% increase in sleeplessness. The results sustain qualitatively under OLS estimation but are attenuated. The analysis provides a previously unaccounted-for benefit of more stringent air quality regulation. It also offers a candidate mechanism in support of recent research that links daily air quality to diminished workplace productivity, cognitive performance, school absence, traffic accidents, and other detrimental outcomes. Chapter 2: We provide linear and non-parametric estimates of the causal impact of short-term exposure to polluted air on the prevalence of cough in a panel of a hundred Chinese cities. In our central estimate, which exploits plausibly-exogenous variations in the number of agricultural fires burning in the vicinity as an instrument, we find that a one standard deviation increase in airborne pollution causes a roughly 5% increase in the prevalence of cough in the affected city. Amongst pollutants the effect can be tied specifically to particulate matter (PM2.5). The results prove resilient in a series of robustness tests and falsification exercises. Chapter 3: We provide the first study of the relationship between air pollution and students' migration intentions for higher education. Young people's interest in local study is proxied by their Baidu search index for local universities. The IV method is supplemented to identify the causal link by instrumenting for particular matter with plausibly exogenous variations in temperature inversion strength. The estimates of effect sizes are substantial and robust. When air quality in Beijing moves from good-day level to moderately-polluted level, people's search for local education decreases by 3.8% under OLS and 11.8% under IV. The results release the signal that people lost their interest in local universities due to the elevated air pollution. There could be future out-migration to cleaner cities for higher education.

The Earth’s atmosphere consists of both gaseous and condensed-phase components, the condensed-phase material is called particulate matter (PM). The effects of atmospheric PM include adverse health impacts, as well as climate forcing. Both qualitative and quantitative knowledge about PM is necessary to assess these effects, and to devise best mitigation strategies. Understanding the distribution of atmospheric particulate matter is complex because much of it is of secondary origin rather than from primary emissions. Furthermore, there are multiple anthropogenic and natural sources of the contributing precursors, and all these processes are influenced by atmospheric conditions and transport. In this work, one of the major constituents of atmospheric PM - carbonaceous aerosol - is studied. A regional application of the EMEP MSC-W atmospheric chemical transport model - EMEP4UK - was used to model air pollution over the British Isles with a horizontal resolution of 5 km x 5 km. One-way nesting was used from the European computational domain of 50 km x 50 km to the finer spatial grid of EMEP4UK. Several model experiments were devised in order to investigate the well-known deficiency that models currently underestimate organic aerosol (OA) concentrations compared with observations. The model experiments were evaluated with comprehensive year-long novel measurements from the Clear Air for London (ClearfLo) campaign in 2012. Several sources of organic aerosol that are either missing, greatly underestimated, or may be spatially misplaced in official emissions inventories were re-evaluated. Firstly, missing diesel-related intermediate volatility organic compound (IVOC) emissions from diesel vehicles derived directly from field measurements at the urban background site during the 2012 ClearfLo campaign were added into the model. According to the model simulations, these diesel-IVOCs can explain on average ~30% of the annual secondary organic aerosol (SOA) in and around London. Furthermore, the 90- th percentile of modelled daily SOA concentrations for the whole year was 3.8 μgm-3, constituting a notable addition to total particulate matter. More measurements of these precursors (currently not included in official emissions inventories) is recommended. Secondly, spatially and temporally resolved emissions of cooking OA (COA; emissions from meat charbroiling, or frying and deep-frying) were developed. These emissions are currently neglected in European emissions inventories, yet measurements point to significant COA contribution to ambient PM concentrations (up to 2.0 μgm-3 on annual average for central London). The final COA emission source strength derived here (320 mg person-1 day-1) was spatially distributed to workday population density (as opposed to residential population density). The impact of COA on surface concentrations is spatially very limited, however, as the modelled concentrations dropped markedly outside of urban areas. For example, annual average modelled concentration for the Harwell location was just 0.1 μgm-3. Thirdly, redistributing 50% of non-industrial wood and coal burning emissions to residential population density (thus over-writing, in part, the assumption made by the national emissions inventory that only smokeless fuels are burned in smoke control areas) increased the modelled solid fuel OA (SFOA) concentration at the London North Kensington site to 0.8 μgm-3, from the Base run value (using the emissions’ spatial distribution and total as officially reported) of just 0.3 μgm-3. For comparison, the measured annual mean concentration of SFOA at this site was 1.0 μgm-3. Based on the model evaluation presented, redistribution of SFOA emissions into smoke control areas is justified, but further refinement of the amount, as well as the temporal emission profile of this component is necessary. The total effect of the three refinements undertaken in this work increased the model estimate of the annual mean OA concentration at the London North Kensington site from 1.8 μgm-3 to 3.8 μgm-3, which is much closer to the observed value of 4.2 μgm-3. Thus, this work has provided relevant insight into the nature and magnitude of missing, under-represented, and spatially inappropriately-distributed emissions of primary OA and OA precursors. Although the study area was focused on pollutant concentrations over the British Isles, all of the components examined here are of great relevance to the air quality in other countries as well — in Europe and globally. Therefore, the inclusion of these improvements into other air quality models and official emissions’ inventories is advised.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Includes bibliographical references. / by Mark Andrew Philip. / M.S.

Heavy air pollution is a global phenomenon that affects both developing and developed countries. While many studies have estimated air pollution’s negative impact on health, no study has shown air pollution to have any impact on countries’ aggregate economic productivity. With a growing body of literature showing that air pollutants may have a significant negative impact on labor productivity, a primary input to a nation’s economic production, I hypothesize and show that ambient air pollution indeed exhibits a significant negative impact on a country’s economic productivity as measured by GDP per capita. In Chapters 1 and 2 of this dissertation, I make identification of the causal relationship between air pollution and GDP per capita using the Huai River Policy and wild res as instruments. Chapter 3 investigates the impact of temperature, another key environmental factor, on labor productivity using a rich data set comprising 4 million baseball pitches. My results provide empirical evidence for modeling economic loss in response to air pollution and climate change.

Chan Pui-Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 169-182). / Text in English; abstracts in English and Chinese. / Chan Pui-Ling. / Acknowledgements --- p.i / Abstracts --- p.ii / Table of Contents --- p.v / List of Plates --- p.ix / List of Figures --- p.xii / List of Tables --- p.xiv / Abbreviations --- p.xviii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Indoor Air Quality (IAQ): An overview --- p.1 / Chapter 1.1.1 --- Importance of indoor air quality --- p.2 / Chapter 1.1.2 --- Common indoor air pollutants --- p.2 / Chapter 1.1.3 --- Airborne bacteria --- p.4 / Chapter 1.1.3.1 --- Possible sources of airborne bacteria --- p.4 / Chapter 1.1.3.2 --- Health effects of the airborne bacteria --- p.5 / Chapter a. --- Sick building syndromes --- p.5 / Chapter b. --- Building-related illness --- p.7 / Chapter 1.1.4 --- Importance of studying airborne bacteria --- p.12 / Chapter 1.2 --- Situation in Hong Kong --- p.13 / Chapter 1.2.1 --- Outdoor air quality --- p.14 / Chapter 1.2.2 --- Indoor air quality --- p.14 / Chapter 1.2.2.1 --- Hong Kong studies --- p.16 / Chapter 1.2.3 --- Air quality objectives in Hong Kong --- p.18 / Chapter 1.3 --- Different sampling methods --- p.18 / Chapter 1.4 --- Identification of bacteria --- p.24 / Chapter 1.5 --- Site selection --- p.26 / Chapter 2 --- Objectives --- p.28 / Chapter 3 --- Materials and methods --- p.29 / Chapter 3.1 --- Samples collection --- p.29 / Chapter 3.1.1 --- Sampling site --- p.29 / Chapter 3.1.2 --- Complete Biosampler System --- p.29 / Chapter 3.1.3 --- Sampling preparation --- p.33 / Chapter 3.1.4 --- Sampling procedures --- p.33 / Chapter 3.2 --- Recovery of the airborne bacteria --- p.36 / Chapter 3.2.1 --- Cultural medium --- p.36 / Chapter 3.2.2 --- Recovery procedures --- p.36 / Chapter 3.2.3 --- Frozen stocks --- p.37 / Chapter 3.3 --- Indentification of bacterial strains --- p.37 / Chapter 3.3.1 --- Gram stain --- p.37 / Chapter 3.3.1.1 --- Chemical reagents --- p.37 / Chapter 3.3.1.2 --- Gram stain procedures --- p.38 / Chapter 3.3.2 --- Oxidase test --- p.38 / Chapter 3.3.2.1 --- Chemical reagents --- p.38 / Chapter 3.3.2.2 --- Oxidase test procedures --- p.41 / Chapter 3.3.3 --- Midi Sherlock® Microbial Identification System (MIDI) --- p.41 / Chapter 3.3.3.1 --- Culture medium --- p.41 / Chapter 3.3.3.2 --- Chemical reagents --- p.41 / Chapter 3.3.3.3 --- MIDI procedures --- p.41 / Chapter 3.3.4 --- Biolog MicroLogTM system (Biolog) --- p.41 / Chapter 3.3.4.1 --- Culture medium --- p.41 / Chapter 3.3.4.2 --- Chemical reagents --- p.44 / Chapter 3.3.4.3 --- Biolog procedures --- p.44 / Chapter 3.3.5 --- DuPont Qualicon RiboPrinter® Microbial Characterization System (RiboPrinter) --- p.46 / Chapter 3.3.5.1 --- Culture medium --- p.46 / Chapter 3.3.5.2 --- Chemical reagents --- p.46 / Chapter 3.3.5.3 --- RiboPrinter procedures --- p.46 / Chapter 4 --- Results --- p.50 / Chapter 4.1 --- Sample naming system --- p.50 / Chapter 4.2 --- Interpretation of results --- p.50 / Chapter 4.2.1 --- Midi Sherlock® Microbial Identification System (MIDI) --- p.51 / Chapter 4.2.2 --- Biolog MicroLog´ёØ System (Biolog) --- p.51 / Chapter 4.2.3 --- DuPont Qualicon RiboPrinter® Microbial Characterization System (RiboPrinter) --- p.52 / Chapter 4.3 --- Sample results --- p.53 / Chapter 4.3.1 --- Sample 1 (Spring) --- p.53 / Chapter 4.3.2 --- Sample 2 (Summer-holiday) --- p.62 / Chapter 4.3.3 --- Sample 3 (Summer-school time) --- p.71 / Chapter 4.3.4 --- Sample 4 (Autumn) --- p.81 / Chapter 4.3.5 --- Sample 5 (Winter) --- p.90 / Chapter 4.4 --- Bacterial profile of the student canteen --- p.100 / Chapter 4.5 --- The cell and colony morphology of the dominant bacteria --- p.100 / Chapter 4.6 --- Comparison between samples --- p.121 / Chapter 4.6.1 --- Spatial variation --- p.121 / Chapter 4.6.1.1 --- Spatial effect on bacterial abundance --- p.121 / Chapter 4.6.1.2 --- Spatial effect on species diversity --- p.121 / Chapter 4.6.2 --- Daily variation --- p.126 / Chapter 4.6.2.1 --- Daily effect on bacterial abundance --- p.126 / Chapter 4.6.2.2 --- Daily effect on species diversity --- p.126 / Chapter 4.6.3 --- Seasonal variation --- p.126 / Chapter 4.6.3.1 --- Seasonal effect on bacterial abundance --- p.126 / Chapter 4.6.3.2 --- Seasonal effect on species diversity --- p.130 / Chapter 4.7 --- Temperature effect on individual airborne bacterial population --- p.130 / Chapter 4.7.1 --- Gram positive bacteria --- p.130 / Chapter 4.7.2 --- Gram negative bacteria --- p.130 / Chapter 4.8 --- Effect of relative humidity on individual airborne bacterial population --- p.137 / Chapter 4.8.1 --- Gram positive bacteria --- p.137 / Chapter 4.8.2 --- Gram negative bacteria --- p.137 / Chapter 5 --- Discussion --- p.143 / Chapter 5.1 --- Bacterial profile --- p.143 / Chapter 5.1.1 --- Bacterial diversity --- p.143 / Chapter 5.1.2 --- Information of the identified bacteria from the student canteen --- p.144 / Chapter 5.1.3 --- Pathogenicity --- p.153 / Chapter 5.1.4 --- Summary on the bacterial profile --- p.153 / Chapter 5.2 --- Comparison between samples --- p.160 / Chapter 5.2.1 --- Spatial variation (Sampling point 1 against Sampling point 2) --- p.160 / Chapter 5.2.2 --- Daily variation (Morning against Afternoon) --- p.161 / Chapter 5.2.3 --- Seasonal variation --- p.162 / Chapter 5.2.4 --- Summer holiday against Summer school time --- p.163 / Chapter 5.2.5 --- Summary on the factors affecting the bacterial content --- p.164 / Chapter 5.3 --- Summary on indoor air quality of the student canteen in terms of bacterial level. --- p.166 / Chapter 6 --- Conclusions --- p.168 / Chapter 7 --- References --- p.169 / Appendix 1 --- p.183 / Appendix 2 --- p.187

Thesis (Ph. D.)--University of Texas Health Science Center at Houston, 2005. / Includes bibliographical references (leaves 376-393). Also available online via the Texas Medical Center website (http://digitalcommons.library.tmc.edu/).

Background: Fraction of exhaled nitric oxide (FENO) is a promising non-invasive index of airway inflammation that may be used to assess respiratory effects of air pollution. We evaluated FENO as a measure of airway inflammation after controlled exposure to diesel exhaust or ozone. Methods: Healthy volunteers were exposed to either diesel exhaust (particle concentration 300 mu g/m(3)) and filtered air for one hour, or ozone (300 ppb) and filtered air for 75 minutes. FENO was measured in duplicate at expiratory flow rates of 10, 50, 100 and 270 mL/s before, 6 and 24 hours after each exposure. Results: Exposure to diesel exhaust increased FENO at 6 hours compared with air at expiratory flow rates of 10 mL/s (p = 0.01) and at 50 mL/s (p = 0.011), but FENO did not differ significantly at higher flow rates. Increases in FENO following diesel exhaust were attenuated at 24 hours. Ozone did not affect FENO at any flow rate or time point. Conclusions: Exposure to diesel exhaust, but not ozone, increased FENO concentrations in healthy subjects. Differences in the induction of airway inflammation may explain divergent responses to diesel exhaust and ozone, with implications for the use of FENO as an index of exposure to air pollution.

No description available.

Many chemicals off-gas or leech into indoor environments from sources such as consumer products, furnishings, and building materials. An understanding of the pathways from the sources to human exposure is vital in order to implement control strategies and lower exposure. Objects containing Brominated flame retardants (BFRs), one class of semivolatile organic compounds (SVOCs), burn more slowly during combustion. BFRs, especially polybrominated diphenyl ethers (PBDEs), are used in electronic devices including casings and circuit boards. Heat from internal circuitry increases the BFR vapor pressure and the partition coefficient, enhancing its transport out of the substrate and into the indoor environment. In this work, a computer tower in an office setting provides a case study to examine the emissions of, and exposure to, PBDEs. the case of a computer tower, the cooling fan increases the mass transfer coefficient, further increasing emissions. During computer use, the emission rate of PBDEs from the interior of the case is more than double the emission rate from the exterior of the case due to elevated internal temperatures and higher mass transfer due to the cooling fan. The concentration of PBDEs in the room air increases 40 - 80% for every 5°C increase inside the computer case, depending on the PBDE congener. Such enhanced emissions are a concern since recent studies have shown adverse health effects of PBDEs on human health. The major contributions of this work are: A model was developed that is useful for SVOC emissions for various heat and mass transfer situations, including diffusion through the slab and convective boundary conditions on each side of the slab, which may be simplified if the situation warrants; A systematic propagation of the uncertainties and variability of the model parameters was accomplished by using a Monte Carlo method for the input of the parameters into the model; A polydisperse indoor particle distribution was used as a sink, identifying the size-discretized particle phase PBDE concentration; An exposure assessment showed that the inhalation pathway for PBDEs in the gas and particulate phases is relevant and that the particulate phase exposure is dominant. / text