Particulate Matter Sampling Techniques and Data Modelling Methods

Particulate matter with 10 μm or less in diameter (PM10) is known to have adverse effects on human health and the environment. For countries committed to reducing PM10 emissions, it is essential to have models that accurately estimate and predict PM10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM10 is presented. This includes the instrumentation used to measure particulate matter, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM10 trends in New Zealand—a remote country of islands in the South Pacific Ocean—are examined. In conclusion, the issues and challenges faced when modelling PM10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM10 prediction and estimation models are presented

Detecting collisions in sets of moving particles: a survey and some experiments

Detecting and responding to collisions between particles is an important requirement for building simulations in computational science. Due to the large number of potential collisions it is impractical to check all possibilities, so the development of algorithms which narrow down the number of possible searches to a small number is important. In this paper we review various algorithms for this task, and give results from a number of experiments which demonstrate the relative efficiency of these algorithms on a fundamental problem of detecting collisions between particles undergoing Brownian motion. The general slant of the paper is towards the development of algorithms for simulating microbiological systems

An Exploration of Novice Programmers' Comprehension of Conditionals in Imperative and Functional Programming

2Students of introductory programming courses are expected to develop higher-order thinking skills to inspect, understand and modify code. However, although novices can correctly write small programs, they appear to lack a more abstract, comprehensive grasp of basic constructs, such as conceiving the overall effect of alternative conditional flows. This work takes a little-explored perspective on the comprehension of tiny programs by asking students to reason about reversing conditionals in either an imperative or a functional context. More specifically, besides deciding if the given constructs can be reversed, students had to justify their choice by writing a reversing program or by providing suitable counterexamples. The students’ answers to four reversibility questions have been analysed through the lens of the SOLO taxonomy. 45% of students correctly identified the reversibility for the four code items; furthermore, more than 50% of each cohort were able to provide correct justifications for at least three of their four answers. Most incorrect answers were due to failures to consider border cases or to edit the conditional expressions appropriately to reverse the construct. Differences in comprehension between functional and imperative languages are explored indicating the explicit else paths of the functional examples facilitate comprehension compared with the implicit else (no update) of its imperative counterpart.partially_openopenMirolo, Claudio; Izu, CruzMirolo, Claudio; Izu, Cru

Going SOLO to assess novice programmers

This paper explores the programming knowledge of novices using Biggs' SOLO taxonomy. It builds on previous work of Lister et al. (2006) and addresses some of the criticisms of that work. The research was conducted by studying the exam scripts for 120 introductory programming students, in which three specific questions were analyzed using the SOLO taxonomy. The study reports the following four findings: when the instruction to students used by Lister et al. - "In plain English, explain what the following segment of Java code does" - is replaced with a less ambiguous instruction, many students still provide multistructural responses; students are relatively consistent in the SOLO level of their answers; student responses on SOLO reading tasks correlate positively with performance on writing tasks; postgraduates students manifest a higher level of thinking than undergraduates. Copyright 2008 ACM

Fostering Program Comprehension in Novice Programmers - Learning Activities and Learning Trajectories

This working group asserts that Program Comprehension (ProgComp) plays a critical part in the process of writing programs. For example, this paper is written from a basic draft that was edited and revised until it clearly presented our idea. Similarly, a program is written incrementally, with each step tested, debugged and extended until the program achieves its goal. Novice programmers should develop program comprehension skills as they learn to code so that they are able both to read and reason about code created by others, and to reflect on their code when writing, debugging or extending it. To foster such competencies our group identified two main goals: (g1) to collect and define learning activities that explicitly address key components of program comprehension and (g2) to define tentative theoretical learning trajectories that will guide teachers as they select and sequence those learning activities in their CS0/CS1/CS2 or K-12 courses. The WG has completed the first goal and laid down a strong foundation towards the second goal as presented in this report. After a thorough literature review, a detailed description of the Block Model is provided, as this model has been used with a dual purpose, to classify and present an extensive list of ProgComp tasks, and to describe a possible learning trajectory for a complex task, covering different cells of the Block Model matrix. The latter is intended to help instructors to decompose complex tasks and identify which aspects of ProgComp are being fostered

A self-consistent, multi-variate method for the determination of gas phase rate coefficients, applied to reactions of atmospheric VOCs and the hydroxyl radical

Gas-phase rate coefficients are fundamental to understanding atmospheric chemistry, yet experimental data are not available for the oxidation reactions of many of the thousands of volatile organic compounds (VOCs) observed in the troposphere. Here a new experimental method is reported for the simultaneous study of reactions between multiple different VOCs and OH, the most important daytime atmospheric radical oxidant. This technique is based upon established relative rate concepts but has the advantage of a much higher throughput of target VOCs. By evaluating multiple VOCs in each experiment, and through measurement of the depletion in each VOC after reaction with OH, the OH + VOC reaction rate coefficients can be derived. Results from experiments conducted under controlled laboratory conditions were in good agreement with the available literature for the reaction of nineteen VOCs, prepared in synthetic gas mixtures, with OH. This approach was used to determine a rate coefficient for the reaction of OH with 2,3-dimethylpent-1-ene for the first time; k = 5.7 (±0.3) × 10–11–cm3 molecule−1 s−1. In addition, a further seven VOCs had only two, or fewer, individual OH rate coefficient measurements available in the literature. The results from this work were in good agreement with those measurements. A similar dataset, at an elevated temperature of 323 (±10) K, was used to determine new OH rate coefficients for twelve aromatic, five alkane, five alkene and three monoterpene VOC + OH reactions. In OH relative reactivity experiments that used ambient air at the University of York, a large number of different VOCs were observed, of which 23 were positively identified. 19 OH rate coefficients were derived from these ambient air samples, including ten reactions for which data was previously unavailable at the elevated reaction temperature of T = 323 (±10) K

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Strong anthropogenic control of secondary organic aerosol formation from isoprene in Beijing

Isoprene-derived secondary organic aerosol (iSOA) is a significant contributor to organic carbon (OC) in some forested regions, such as tropical rainforests and the Southeastern US. However, its contribution to organic aerosol in urban areas that have high levels of anthropogenic pollutants is poorly understood. In this study, we examined the formation of anthropogenically influenced iSOA during summer in Beijing, China. Local isoprene emissions and high levels of anthropogenic pollutants, in particular NOx and particulate SO2-4 , led to the formation of iSOA under both high- A nd low-NO oxidation conditions, with significant heterogeneous transformations of isoprene-derived oxidation products to particulate organosulfates (OSs) and nitrooxyorganosulfates (NOSs). Ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry was combined with a rapid automated data processing technique to quantify 31 proposed iSOA tracers in offline PM2.5 filter extracts. The co-elution of the inorganic ions in the extracts caused matrix effects that impacted two authentic standards differently. The average concentration of iSOA OSs and NOSs was 82.5 ngm-3, which was around 3 times higher than the observed concentrations of their oxygenated precursors (2-methyltetrols and 2-methylglyceric acid). OS formation was dependant on both photochemistry and the sulfate available for reactive uptake, as shown by a strong correlation with the product of ozone (O3) and particulate sulfate (SO2-4). A greater proportion of high-NO OS products were observed in Beijing compared with previous studies in less polluted environments. The iSOA-derived OSs and NOSs represented 0.62% of the oxidized organic aerosol measured by aerosol mass spectrometry on average, but this increased to ∼ 3% on certain days. These results indicate for the first time that iSOA formation in urban Beijing is strongly controlled by anthropogenic emissions and results in extensive conversion to OS products from heterogenous reactions

Low-NO atmospheric oxidation pathways in a polluted megacity

The impact of emissions of volatile organic compounds (VOCs) to the atmosphere on the production of secondary pollutants, such as ozone and secondary organic aerosol (SOA), is mediated by the concentration of nitric oxide (NO). Polluted urban atmospheres are typically considered to be “high-NO” environments, while remote regions such as rainforests, with minimal anthropogenic influences, are considered to be “low NO”. However, our observations from central Beijing show that this simplistic separation of regimes is flawed. Despite being in one of the largest megacities in the world, we observe formation of gas- and aerosol-phase oxidation products usually associated with low-NO “rainforest-like” atmospheric oxidation pathways during the afternoon, caused by extreme suppression of NO concentrations at this time. Box model calculations suggest that during the morning high-NO chemistry predominates (95 %) but in the afternoon low-NO chemistry plays a greater role (30 %). Current emissions inventories are applied in the GEOS-Chem model which shows that such models, when run at the regional scale, fail to accurately predict such an extreme diurnal cycle in the NO concentration. With increasing global emphasis on reducing air pollution, it is crucial for the modelling tools used to develop urban air quality policy to be able to accurately represent such extreme diurnal variations in NO to accurately predict the formation of pollutants such as SOA and ozone