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This paper aims to design and optimize the threaded fastener of leakage current particulate matter (PM) sensor. The corresponding air-tight test is conducted to ensure the reliability of the installation strategy with screw connection.

Research on the pressure-deformation curve of seal gasket was conducted and the vibration load of engine was considered for the calculation of the minimum installation pre-tightening force. Simultaneously, the danger threaded section area was calculated, and the carrying capacity was verified. The height of the welding line was studied to ensure the reliability of the application. FEA was carried out to acquire the relationship between local structure size and local stress for continuous improvement of thread connection. The installation torque range was acquired from the torque control principle for the pre-tightening force. The sealing reliability of the connector was proved with leakage.

The air tightness of the thread connector is proved to be fine. When the pre-tightening force is over 8,000 N, and its length reaches 2 mm, the connector has good reliability at ambient temperature. The tightening torque of 60-74 Nm can guarantee the reliable fixing ability of thread connector, and its plastic non-deformation ability in the process of repeated tearing down.

This paper provides an installation strategy and an optimization of PM sensor, which has a positive effect on the study and the manufacture of PM sensor. It is helpful to further develop PM sensor and after-treatment technology. This kind of real-time monitoring PM sensor needs to be studied further to achieve its commercial application.

The noise and dust particles caused by the construction transport are by most stakeholders experienced as disturbing. The purpose of this study is to explore how sonification can support visualization in construction planning to decrease construction transport disturbances.

This paper presents an interdisciplinary research project, combining research on construction logistics, internet of things and sonification. First, a data recording device, including sound, particle, temperature and humidity sensors, was implemented and deployed in a development project. Second, the collected data were used in a sonification design, which was, third, evaluated with potential users.

The results showed that the low-cost sensors used could capture “good enough” data, and that the use of sonification for representing these data is interesting and a possible useful tool in urban and construction transport planning.

There is a need to further evolve the sonification design and better communicate the aim of the sounds used to potential users. Further testing is also needed.

This study introduces new ideas of how to support visualization with sonification planning the construction work and its impact on the vicinity of the site. Currently, urban planning and construction planning focus on visualizing the final result, with little focus on how to handle disturbances during the construction process.

Showing the potentials of using low-cost sensor data in sonification, and using sonification together with visualization, is the result of a novel interdisciplinary research area combination.

The paper aims to develop a particle matter (PM_2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM_2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM_2.5 require either time-consuming or expensive equipment, thus making PM_2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.

This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.

The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.

This work demonstrates that by combining low-cost sensors and ML, indoor PM_2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.

The purpose of this paper is to demonstrate a novel 3D system-in-package (SiP) approach. This new packaging approach is based on stacked silicon submount technology. As demonstrators, a smart lighting module and a sensor systems were successfully developed by using the fabrication and assembly process described in this paper.

The stacked module consists of multiple layers of silicon submounts which can be designed and fabricated in parallel. The 3D stacking design offers higher silicon efficiency and miniaturized package form factor. This platform consists of silicon submount design and fabrication, module packaging, system assembling and testing and analyzing.

In this paper, a smart light emitting diode system and sensor system will be described based on stacked silicon submount and 3D SiP technology. The integrated smart lighting module meets the optical requirements of general lighting applications. The developed SiP design is also implemented into the miniaturization of particular matter sensors and gas sensor detection system.

SiP has great potential of integrating multiple components into a single compact package, which has potential implementation in intelligent applications.

The purpose of this paper is to provide local environmental information to raise community’s environmental awareness, as a cornerstone to improve the quality of the built environment. Next to that, it provides environmental information to professionals and academia in the fields of urbanism and urban microclimate, making it available for reuse.

The wireless sensor network (WSN) consists of sensor platforms deployed at fixed locations in the urban environment, measuring temperature, humidity, noise and air quality. Measurements are transferred to a server via long range wide area network (LoRaWAN). Data are also processed and publicly disseminated via the server. The WSN is made interactive as to increase user involvement, i.e. people who pass by a physical sensor in the city can interact with the sensor platform and request specific environmental data in near real time.

Microclimate phenomena such as temperature, humidity and air quality can be successfully measured with a WSN. Noise measurements are less suitable to send over LoRaWAN due to high temporal variations.

Further testing and development of the sensor modules is needed to ensure consistent measurements and data quality.

Due to time and budget limitations for the project group, it was not possible to gather reliable data for noise and air quality. Therefore, conclusions on the effect of the measurements on the built environment cannot currently be drawn.

An autonomously working low-cost low-energy WSN gathering near real-time environmental data is successfully deployed. Ensuring data quality of the measurement results is subject for upcoming research.

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m² cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

The purpose of this paper is to present an optical technique for the condition monitoring of synthetic hydraulic oil; a deviation from the current techniques based on electrical principles which could be masked by wear particles and polar contaminants in oil.

Color‐change detecting device was developed using light‐emitting diodes, optic fibers and photodiodes of three‐color‐sensing elements. Color ratio (CR) and total contamination parameters based on transmitted light intensity in red, green, and blue wavelengths were used for oil chemical and particulate contamination assessment.

CR criterion was found independent of the particulate contamination of oil; but depended on chemical degradation. Total contamination index of the device depended on both the chemical degradation and particulate contamination of the oil, being most sensitive in blue wavelength, and least in the red. Test results for synthetic hydraulic oils monitored corroborated with results of viscosity, total acid number, RDE emission spectrometry, particulate counts and UV‐Vis photospectrometry. CR showed a clearer indication of oil degradation, compared to key monitoring parameters such as total acid number, viscosity, RDE emission spectrometry and particle counts.

This paper demonstrates how oil chemical degradation and total contamination could be detected through the device, before incipient wear occurs at tribological interfaces. The results showed that the color‐change parameters are effective criteria for the condition monitoring of synthetic hydraulic oils.

New Zealand’s historical housing stock comprises largely single-storey detached houses, characterised by poor winter comfort with high air infiltration. Challenges with affordability and land use are shifting New Zealand’s housing stock towards double-storey, conjoined medium-density housing (MDH). Reduced external surfaces in this typology should reduce winter heat loss and infiltration, improving winter comfort and health. New concerns arise, however, regarding summertime overheating and poor indoor air quality.

A field study was undertaken where temperature, humidity, airtightness, particulate matter (PM) and total volatile organic compounds (TVOC) were measured in two unoccupied, newly built double-storey, conjoined houses, for several weeks over summer.

The reduced surface area of this typology did not reduce infiltration and demonstrated significant periods of overheating. Internal PM concentrations generally exceeded outdoor concentrations but did not exceed annual average outdoor PM10 guidelines of 20 µg m-3. Infiltration factors (Finf) were closer to more traditional houses. TVOC readings varied widely, but frequently exceeded international guidelines.

The small sample limits the applications of conclusions more widely. Recommendations to investigate a wider sample in different locations with more detailed VOC analysis over all seasons are made.

Improvements to internal environments cannot be guaranteed by housing typology changes alone and must still involve thoughtful environmental design.

Housing typology changes may not improve internal living environments.

A move to the new MDH typology may not achieve expectations of airtightness and thermal improvement. New challenges arise from significant overheating and high TVOC levels, which may lead to new negative health effects.