Assessment of meteorological settings on air quality modeling system - a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO2 dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Region (RJMR), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well-represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes, so that the largest SO2 concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable.

Subjective and Objective Quality Assessment of Swimming Pool Images

As the research basis of image processing and computer vision research, image quality evaluation (IQA) has been widely used in different visual task fields. As far as we know, limited efforts have been made to date to gather swimming pool image databases and benchmark reliable objective quality models, so far. To filled this gap, in this paper we reported a new database of underwater swimming pool images for the first time, which is composed of 1500 images and associated subjective ratings recorded by 16 inexperienced observers. In addition, we proposed a main target area extraction and multi-feature fusion image quality assessment (MM-IQA) for a swimming pool environment, which performs pixel-level fusion for multiple features of the image on the premise of highlighting important detection objects. Meanwhile, a variety of well-established full-reference (FR) quality evaluation methods and partial no-reference (NR) quality evaluation algorithms are selected to verify the database we created. Extensive experimental results show that the proposed algorithm is superior to the most advanced image quality models in performance evaluation and the outcomes of subjective and objective quality assessment of most methods involved in the comparison have good correlation and consistency, which further indicating indicates that the establishment of a large-scale pool image quality assessment database is of wide applicability and importance.

ESQuMo an Embedded Software Quality Model

Embedded systems are increasingly used in our daily life due to their importance. They are computer platforms consisting of hardware and software. They run specific tasks to realize functional and non functional requirements. Several specific quality attributes were identified as relevant to the embedded system domain. However, the existent general quality models do not address clearly these specific quality attributes. Hence, the proposition of quality models which address the relevant quality attributes of embedded systems needs more attention and investigation. The major goal of this paper is to propose a new quality model (called ESQuMo for Embedded Software Quality Model) which provides a better understanding of quality in the context of embedded software. Besides, it focuses the light on the relevant attributes of the embedded software and addresses clearly the importance of these attributes. In fact, ESQuMo is based on the well-established ISO/IEC 25010 standard quality model.

Evaluating Quality Characteristics of Ubiquitous Application Through Means of Quality Models using Meta-metrics Approach

Ubiquitous computing shifted the way how users interact with applications. The demand of information anytime and anywhere impacts the daily life of its users, be it work related or personal. Difficulty arises when determining the quality of ubiquitous application due to lack in appropriate metrics of quality models, which serves as the motivation behind this paper. The aim of this paper is to assess the quality of ubiquitous application using comparative analysis of quality model metrics via meta-metrics approach. Preliminary review mapping was conducted where distinctive quality characteristics of ubiquitous applications from AQUARIUM model are identified. Metrics mapping was then conducted to compare metrics characteristics with quality characteristics via value assignment using meta-metrics technique. Results shows that most of the metrics mapped are not of definitive derivation, providing opportunity to have a more structured and defined measurement function.

Improving and parameterising nitrogen and phosphorus modelling for application of LUCI in New Zealand

<p>Over the last 50 years freshwater and marine environments have become severely impaired due to contamination from pathogens, heavy metals, sediment, industrial chemicals and nutrients (MEA 2005b). In many countries, including New Zealand, increased nitrogen (N) and phosphorus (P) loading to terrestrial and freshwater environments from diffuse nutrient sources are of particular concern (MEA 2005a; PCE 2015b; Steffen et al. 2015) and many governments now mandate control of diffuse nutrient loss to water. Water quality models are invaluable tools that can assist with decision making around this widespread issue through exploration of the current situation and future scenarios. Many water quality models exist, functioning at a variety of temporal and spatial scales and varying in detail and complexity. However, few, if any, simultaneously represent sub-field to catchment scale processes and outcomes, both of which are required to fully address water quality issues associated with diffuse nutrient sources. Those that do, likely require extensive time and expertise to operate. Water quality models embedded in the Land Utilisation and Capability Indicator (LUCI), an ecosystem service decision support framework, offer the opportunity to overcome these limitations. Being highly spatially explicit, yet straightforward to use, they can inform and assist individual land owners, catchment managers and other stakeholders with planning, decision making and management of water quality at sub-field to landscape scale. To model diffuse nutrient losses LUCI, like many catchment scale water quality models, requires some form of estimated nutrient loss, or export coefficient, from land units within the catchment of interest. To be representative export coefficients must consider climate, soil, topography, and land cover and management variables. A number of methods of export coefficient derivation exist, although generally they consider only very limited geo-climatic, land cover and land management variables. The principal aim of this study is development of algorithms capable of calculating New Zealand site specific N and P export coefficients from detailed geo-climatic, land cover and land management variables, for application in LUCI water quality models. Algorithms for pastoral land cover are developed from a large dataset comprising real pastoral farm input and output data from nutrient budgeting model OVERSEER. Algorithms are extended to land covers other than pasture, albeit in a limited manner. This is achieved through rescaling of the pastoral algorithms to account for relative differences in literature reported N and P losses from pasture and a variety of other New Zealand land covers. Application of the developed algorithms in LUCI water quality models results in positioning of export coefficients at the DEM grid square scale (≤ 15 m x 15 m for New Zealand). In addition, intra-basin configuration is considered in LUCI, at the same grid square scale, as water and nutrient flows are cascaded through the catchment. Application of the export coefficient calculating algorithms are applied to two contrasting New Zealand catchments. Tuapaka catchment, an 85ha agricultural foothill catchment in Manawatu, North Island, and Lake Rotorua catchment, a 502 km2 volcanic, mixed land cover catchment in Bay of Plenty, North Island. This research is supported by Ravensdown, a farmer owned co-operative, which plans to use LUCI extensively to advise and assist farmers with water quality issues. The ability to model mitigation strategies in LUCI is an important capability. Therefore, this research also includes a review of five particularly important on-farm mitigation strategies, which will later be used by the wider LUCI development team to assist with better parameterisation and improved performance of mitigation options in LUCI. Application of the developed algorithms at farm to catchment scale in LUCI results in considerably more nuanced, detailed maps and data showing N and P sources and pathways, compared to LUCI’s previously used ‘one export coefficient per land cover’ approach. Although results indicate absolute nutrient loss values are not always ‘correct’ compared to either OVERSEER predictions or in-stream water quality measurements, these differences appear comparable to those seen with similar water quality models. In addition, the issue of representativeness of OVERSEER predictions and in-stream water quality measurements exists. Nevertheless improvement to absolute predictions is always an aim. This research indicates further improvements to LUCI water quality predictions could result from refinement of both pastoral and other land cover algorithms, and from improved representation of attenuation processes in LUCI, including groundwater representation. However, lack of measured on-land and in-stream N and P loss data is a major challenge to both algorithm refinement and to evaluation of results. In addition, more detailed spatial data would provide more nuanced results from algorithm application. Although the algorithm application context in this research is LUCI water quality models applied in New Zealand, this does not preclude application of the developed algorithms in other export coefficient based, catchment scale water quality models. Using spatial data pertaining to climate, soil, topographic and land management variables, land units of combined variables can be identified and the algorithms applied, resulting in explicitly positioned export coefficients that can be fed into the catchment scale water quality model of interest. Therefore, developments made here potentially represent a wider contribution to catchment scale modelling using export coefficients.</p>

Improving and parameterising nitrogen and phosphorus modelling for application of LUCI in New Zealand

<p>Over the last 50 years freshwater and marine environments have become severely impaired due to contamination from pathogens, heavy metals, sediment, industrial chemicals and nutrients (MEA 2005b). In many countries, including New Zealand, increased nitrogen (N) and phosphorus (P) loading to terrestrial and freshwater environments from diffuse nutrient sources are of particular concern (MEA 2005a; PCE 2015b; Steffen et al. 2015) and many governments now mandate control of diffuse nutrient loss to water. Water quality models are invaluable tools that can assist with decision making around this widespread issue through exploration of the current situation and future scenarios. Many water quality models exist, functioning at a variety of temporal and spatial scales and varying in detail and complexity. However, few, if any, simultaneously represent sub-field to catchment scale processes and outcomes, both of which are required to fully address water quality issues associated with diffuse nutrient sources. Those that do, likely require extensive time and expertise to operate. Water quality models embedded in the Land Utilisation and Capability Indicator (LUCI), an ecosystem service decision support framework, offer the opportunity to overcome these limitations. Being highly spatially explicit, yet straightforward to use, they can inform and assist individual land owners, catchment managers and other stakeholders with planning, decision making and management of water quality at sub-field to landscape scale. To model diffuse nutrient losses LUCI, like many catchment scale water quality models, requires some form of estimated nutrient loss, or export coefficient, from land units within the catchment of interest. To be representative export coefficients must consider climate, soil, topography, and land cover and management variables. A number of methods of export coefficient derivation exist, although generally they consider only very limited geo-climatic, land cover and land management variables. The principal aim of this study is development of algorithms capable of calculating New Zealand site specific N and P export coefficients from detailed geo-climatic, land cover and land management variables, for application in LUCI water quality models. Algorithms for pastoral land cover are developed from a large dataset comprising real pastoral farm input and output data from nutrient budgeting model OVERSEER. Algorithms are extended to land covers other than pasture, albeit in a limited manner. This is achieved through rescaling of the pastoral algorithms to account for relative differences in literature reported N and P losses from pasture and a variety of other New Zealand land covers. Application of the developed algorithms in LUCI water quality models results in positioning of export coefficients at the DEM grid square scale (≤ 15 m x 15 m for New Zealand). In addition, intra-basin configuration is considered in LUCI, at the same grid square scale, as water and nutrient flows are cascaded through the catchment. Application of the export coefficient calculating algorithms are applied to two contrasting New Zealand catchments. Tuapaka catchment, an 85ha agricultural foothill catchment in Manawatu, North Island, and Lake Rotorua catchment, a 502 km2 volcanic, mixed land cover catchment in Bay of Plenty, North Island. This research is supported by Ravensdown, a farmer owned co-operative, which plans to use LUCI extensively to advise and assist farmers with water quality issues. The ability to model mitigation strategies in LUCI is an important capability. Therefore, this research also includes a review of five particularly important on-farm mitigation strategies, which will later be used by the wider LUCI development team to assist with better parameterisation and improved performance of mitigation options in LUCI. Application of the developed algorithms at farm to catchment scale in LUCI results in considerably more nuanced, detailed maps and data showing N and P sources and pathways, compared to LUCI’s previously used ‘one export coefficient per land cover’ approach. Although results indicate absolute nutrient loss values are not always ‘correct’ compared to either OVERSEER predictions or in-stream water quality measurements, these differences appear comparable to those seen with similar water quality models. In addition, the issue of representativeness of OVERSEER predictions and in-stream water quality measurements exists. Nevertheless improvement to absolute predictions is always an aim. This research indicates further improvements to LUCI water quality predictions could result from refinement of both pastoral and other land cover algorithms, and from improved representation of attenuation processes in LUCI, including groundwater representation. However, lack of measured on-land and in-stream N and P loss data is a major challenge to both algorithm refinement and to evaluation of results. In addition, more detailed spatial data would provide more nuanced results from algorithm application. Although the algorithm application context in this research is LUCI water quality models applied in New Zealand, this does not preclude application of the developed algorithms in other export coefficient based, catchment scale water quality models. Using spatial data pertaining to climate, soil, topographic and land management variables, land units of combined variables can be identified and the algorithms applied, resulting in explicitly positioned export coefficients that can be fed into the catchment scale water quality model of interest. Therefore, developments made here potentially represent a wider contribution to catchment scale modelling using export coefficients.</p>

Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality

Atmospheric aerosols can exert influence on meteorology and air quality through aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) and this two-way feedback has been studied by applying two-way coupled meteorology and air quality models. As one of regions with high aerosol loading in the world, Asia has attracted many researchers to investigate the aerosol effects with several two-way coupled models (WRF-Chem, WRF-CMAQ, GRAPES-CUACE and WRF-NAQPMS) over the last decade. This paper attempts to offer bibliographic analysis regarding the current status of applications of two-way coupled models in Asia, related research focuses, model performances and the effects of ARI or/and ACI on meteorology and air quality. There are total 157 peer-reviewed articles published between 2010 and 2019 in Asia meeting the inclusion criteria, with more than 81 % of papers involving the WRF-Chem model. The number of relevant publications has an upward trend annually and East Asia, India, China, as well as the North China Plain are the most studied areas. The effects of ARI and both ARI and ACI induced by natural aerosols (particularly mineral dust) and anthropogenic aerosols (bulk aerosols, different chemical compositions and aerosols from different sources) are widely investigated in Asia. Through the meta-analysis of surface meteorological and air quality variables simulated by two-way coupled models, the model performance affected by aerosol feedbacks depends on different variables, simulation time lengths, selection of two-way coupled models, and study areas. Future research perspectives with respect to the development, improvement, application, and evaluation of two-way coupled meteorology and air quality models are proposed.

Study of Planetary Boundary Layer and Aerosol Transport Using Ceilometer and Air Quality Modelling in New South Wales (NSW), Australia

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and hence the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometer, a lidar instrument to measure cloud base height, has been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in the New South Wales, Australia was used to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ) as well as to understand the aerosol transport from sources to receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results show that the PBLH prediction by the two air quality models corresponds reasonably well with observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate with back trajectory analysis of transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol source from north and north west of Merriwa in remote NSW where windblown dust is the main source, and the other two had sources from south and south east of Merriwa where anthropogenic sources dominate,

Designing Chest X-ray Datasets for Improving Lung Nodules Detection Through Convolutional Neural Networks

In this paper, we propose a method for building alternative training datasets for lung nodule detection from plain chest X-ray images. Our aim is to improve the classification quality of a state-of-the-art CNN by just selecting appropriate samples from the existing datasets. The hypothesis of this research is that high quality models need to learn by contrasting very clean images with those containing nodules, specially those difficult to identify by non-expert clinicians. Current chest X-ray datasets mostly include images where more than one pathology exist and/or contain devices like catheters. This is because most samples come from old people which are the usual patients subject to X-ray examinations. In this paper, we evaluate several combinations of samples from existing datasets in the literature. Results show a great gain in performance for some of the evaluated combinations, confirming our hypothesis. The achieved performance of these models allows a considerable speed-up in the screening of patients by radiologist.