Construction activities generate a large amount of dust and cause significant impacts on air quality of surrounding areas. Thus, revealing the characteristics of construction dust is crucial for finding the way of reducing its effects. To fully uncover the characteristics of construction dust affecting surrounding areas, this study selected seven representative construction sites in Qingyuan city, China as empirical cases for field evaluation. In the experiment, the up-downwind method was adopted to monitor and collect TSP (total suspended particulate), PM₁₀ and PM_2.5 (particulate matter ≤10 µm and 2.5 µm in aerodynamic diameter, respectively) concentrations, meteorological data and construction activities of each site for 2 to 3 days and 18 h in a day. The results show that the average daily construction site makes the surrounding areas’ concentration of TSP, PM₁₀ and PM_2.5 increase by 42.24%, 19.76% and 16.27%, respectively. The proportion of TSP, PM₁₀ and PM_2.5 in building construction dust is 1, 0.239 and 0.116, respectively. The large diameter particulate matter was the major constituent and the distance of its influence was limited. In addition, construction vehicles were one of the main influencing factors for building construction dust. However, building construction dust was not significantly correlated with any single meteorological factor when it did not change too much. Findings of this research can provide a valuable basis for reducing the impact of building construction dust on surrounding areas.

The bulk of the particulate matter (PM) emissions generated during construction projects are significantly released during the earthwork and foundation stages. To reduce and control these emissions, it is necessary to have reliable data on their characteristics. However, construction PM are poorly characterized because their composition depends on several factors (e.g., weather and reduction measures) and various on-site activities whose effects may interact. To address these challenges, a long-term quantitative empirical study using advanced statistical methods was performed on a real construction project during the whole earthwork and foundation stages. The upwind-downwind method was used to collect data on PM emissions throughout the earthwork and foundation construction process, and correlation analysis, paired samples t-test, and partial least squares regression (PLS) were used to analyze TSP, PM₁₀, and PM_2.5 emissions and their relationships with various influencing factors. The results showed that both earthwork and foundation constructions generate substantial PM emissions because there were differences with statistical significances in the PM levels measured upwind and downwind of the construction site. TSP and PM₁₀ emissions correlated moderately with humidity and wind speed. However, temperature and atmospheric pressure did not correlate significantly with any of the measured emissions. The main activities responsible for PM emissions during the earthwork and foundation construction stages were hammer piling, waste stacking, and materials transportation. Water spraying was found to effectively reduce TSP and PM₁₀ emissions, while the use of a fog cannon more effectively reduced PM_2.5 emissions. Construction PM is an important source of atmospheric pollution in cities; the findings presented herein provide cornerstone and knowledge to guide efforts for reducing its impact.

Construction contractors play a vital role in reducing the environmental impacts during the construction phase. To mitigate these impacts, contractors need to develop environmentally friendly plans that have optimal equipment, materials and labour configurations. However, construction plans with optimal environment may negatively affect the project cost and duration, resulting in dilemma for contractors on adopting low impacts plans. Moreover, the enumeration method that is usually used needs to assess and compare the performances of a great deal of scenarios, which seems to be time consuming for complicated projects with numerous scenarios. This study therefore developed an integrated method to efficiently provide contractors with plans having optimal environment–cost–time performances. Discrete-event simulation (DES) and particle swarm optimisation algorithms (PSO) are integrated through an iterative loop, which remarkably reduces the efforts on optimal scenarios searching. In the integrated method, the simulation module can model the construction equipment and materials consumption; the assessment module can evaluate multi-objective performances; and the optimisation module fast converges on optimal solutions. A prototype is developed and implemented in a hotel building construction. Results show that the proposed method greatly reduced the times of simulation compared with enumeration method. It provides the contractor with a trade-off solution that can average reduce 26.9% of environmental impact, 19.7% of construction cost, and 10.2% of project duration. The method provides contractors with an efficient and practical decision support tool for environmentally friendly planning.

The building performance simulation (BPS) based on physical models is a popular method to estimate the expected energy-savings of energy-efficient building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. Incomplete information generally comes from the information that is missing, such as the U-value of part building components, due to incomplete documentation or component deterioration over time. It also comes from the case-specific incomplete information due to different documentation systems. Motivated by the available big data of real-life building performance datasets (BPDs), a data-driven approach is proposed to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach constructed a Performance Modelling with Data Imputation (PMDI) with integrated backpropagation neural networks, fuzzy C-means clustering, principal component analysis, and trimmed scores regression. An empirical study was conducted on real-life buildings in Sweden, and the results validated that the PMDI method can model the performance ranges of energy-efficient retrofitting for family house buildings with more than 90% confidence. For a target building in Stockholm, the suggested retrofitting measure is expected to save energy by 12,017~17,292 KWh/year.