Analysis of airborne particulate matter (PM2.5) over Hong Kong using remote sensing and GIS

Applying and Comparing LSTM and ARIMA to Predict CO Levels for a Time-Series Measurements in a Port Area

Air pollution is a major problem in the everyday life of citizens, especially air pollution in the transport domain. Ships play a significant role in coastal air pollution, in conjunction with transport mobility in the broader area of ports. As such, ports should be monitored in order to assess air pollution levels and act accordingly. In this paper, we obtain CO values from environmental sensors that were installed in the broader area of the port of Igoumenitsa in Greece. Initially, we analysed the CO values and we have identified some extreme values in the dataset that showed a potential event. Thereafter, we separated the dataset into 6-h intervals and showed that we have an extremely high rise in certain hours. We transformed the dataset to a moving average dataset, with the objective being the reduction of the extremely high values. We utilised a machine-learning algorithm, namely the univariate long short-term memory (LSTM) algorithm to provide the predicted outcome of the time series from the port that has been collected. We performed experiments by using 100, 1000, and 7000 batches of data. We provided results on the model loss and the root-mean-square error as well as the mean absolute error. We showed that with the case with batch number equals to 7000, the LSTM we achieved a good prediction outcome. The proposed method was compared with the ARIMA model and the comparison results prove the merit of the approach.

The Observation and Characterisation of Fluorescent Bioaerosols Using Real-Time UV-LIF Spectrometry in Hong Kong from June to November 2018

Hong Kong is an area of complex topography, with mixtures of urban and greenbelt spaces. Local bioaerosol concentrations are multifaceted, depending on seasonal variations of meteorological conditions and emission sources. This study is the first known attempt at both quantitatively measuring and identifying airborne bioaerosol contributions, by utilising multiple single particle ultraviolet light-induced fluorescence spectrometers. Based in the Hong Kong University of Science and Technology’s super-site, a WIBS-NEO and PLAIR Rapid-E were operated from June to November, 2018. The purpose of this long-term campaign was to observe the shift in wind patterns and meteorological conditions as the seasons changed, and to investigate how, or if, this impacted on the dispersion and concentrations of bioaerosols in the area. Bioaerosol concentrations based on the particle auto-fluorescence spectra remained low through the summer and autumn months, averaging 4.2 L−1 between June and October. Concentrations were greatest in October, peaking up to 23 L−1. We argued that these concentrations were dominated by dry-weather fungal spores, as evidenced by their spectral profile and relationship with meteorological variables. We discuss potential bioaerosol source regions based on wind-sector cluster analysis and believe that this study paints a picture of bioaerosol emissions in an important region of the world.

Characteristics of Fine Particulate Matter (PM2.5) over Urban, Suburban, and Rural Areas of Hong Kong

In urban areas, fine particulate matter (PM2.5) associated with local vehicle emissions can cause respiratory and cardiorespiratory disease and increased mortality rates, but less so in rural areas. However, Hong Kong may be a special case, since the whole territory often suffers from regional haze from nearby mainland China, as well as local sources. Therefore, to understand which areas of Hong Kong may be affected by damaging levels of fine particulates, PM2.5 data were obtained from March 2005 to February 2009 for urban, suburban, and rural air quality monitoring stations; namely Central (city area, commercial area, and urban populated area), Tsuen Wan (city area, commercial area, urban populated, and residential area), Tung Chung (suburban and residential area), Yuen Long (urban and residential area), and Tap Mun (remote rural area). To evaluate the relative contributions of regional and local pollution sources, the study aimed to test the influence of weather conditions on PM2.5 concentrations. Thus, meteorological parameters including temperature, relative humidity, wind speed, and wind directions were obtained from the Hong Kong Observatory. The results showed that Hong Kong’s air quality is mainly affected by regional aerosol emissions, either transported from the land or ocean, as similar patterns of variations in PM2.5 concentrations were observed over urban, suburban, and rural areas of Hong Kong. Only slightly higher PM2.5 concentrations were observed over urban sites, such as Central, compared to suburban and rural sites, which could be attributed to local automobile emissions. Results showed that meteorological parameters have the potential to explain 80% of the variability in daily mean PM2.5 concentrations—at Yuen Long, 77% at Tung Chung, 72% at Central, 71% at Tsuen Wan, and 67% at Tap Mun, during the spring to summer part of the year. The results provide not only a better understanding of the impact of regional long-distance transport of air pollutants on Hong Kong’s air quality but also a reference for future regional-scale collaboration on air quality management.

Field assessment of the effects of land-cover type and pattern on PM10 and PM2.5 concentrations in a microscale environment

The microscale environment is a very important human-scale outdoor spatial unit. Aimed at investigating the effects of microscale land-cover type and pattern on levels of PM₁₀ and PM_2.5, we monitored PM₁₀ and PM_2.5 concentrations among different land-cover type and pattern sites through field measurements, during four seasons (December 2015 to November 2016) in Beijing, China. Differences of daily PM₁₀ and PM_2.5 concentrations among seven typical land-cover types, and correlations between daily two-sized PM levels and various microscale land-cover patterns as explained by landscape metrics were analyzed. Results show that concentrations of the two-sized particles had stable daytime and seasonal trends. During the four seasons, there were various differences in daily PM₁₀ and PM_2.5 levels among the seven land-cover types. Overall, bare soil always had the highest daily PM₁₀ level, whereas high canopy density vegetation and water bodies had low levels. Maximum PM_2.5 levels were always found in high canopy density vegetation. Moderate canopy density vegetation and water bodies had lower concentrations. Correlations between different landscape metrics and daily levels of two-sized PM varied by season. Metrics reflecting the dominance and distribution of land-cover classifications had closer relationships with particle concentrations in the microscale environment. The patterns of pavement along with low and moderate canopy density vegetation had a greater impact on PM₁₀ level. The responses of PM_2.5 level to patterns of building and low and moderate canopy density vegetation were sensitive. Reasonable design of land-cover structure would be conducive to ameliorate air particle concentrations in the microscale environment. Graphical abstract ᅟ.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Zoning of air quality index (PM10 and PM2.5) by Arc-GIS for Khorramabad city, Iran

Nowadays in many countries, air pollution is one of the major issues affecting human health. Among the various air pollutants particulate matters are mainly present in ambient air pollution. The purpose of this study was to measure the concentration of particulate matter (PM) (namely PM_2.5 and PM₁₀) and to conduct zoning via GIS software in Khorramabad city (Summer - 2017). According to the findings, the average concentrations of PM_2.5 in July, August and September were 100.1, 116.3, and 199.8 μg/m³, respectively. Furthermore, the average concentrations of PM₁₀ in July, August and September were 199.8, 215.7, and 190.8 μg/m³, respectively. The findings of this study also indicated that due to continuous dust storms,particularly in recent years, the air pollution status in Khorramabad was not suitable that can adversely affect public health.

Developing Street-Level PM2.5 and PM10 Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors

Monitoring street-level particulates is essential to air quality management but challenging in high-density Hong Kong due to limitations in local monitoring network and the complexities of street environment. By employing vehicle-based mobile measurements, land use regression (LUR) models were developed to estimate the spatial variation of PM2.5 and PM10 in the downtown area of Hong Kong. Sampling runs were conducted along routes measuring a total of 30 km during a selected measurement period of total 14 days. In total, 321 independent variables were examined to develop LUR models by using stepwise regression with PM2.5 and PM10 as dependent variables. Approximately, 10% increases in the model adjusted R(2) were achieved by integrating urban/building morphology as independent variables into the LUR models. Resultant LUR models show that the most decisive factors on street-level air quality in Hong Kong are frontal area index, an urban/building morphological parameter, and road network line density and traffic volume, two parameters of road traffic. The adjusted R(2) of the final LUR models of PM2.5 and PM10 are 0.633 and 0.707, respectively. These results indicate that urban morphology is more decisive to the street-level air quality in high-density cities than other cities. Air pollution hotspots were also identified based on the LUR mapping.

Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration

This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail.

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

A temperature inversion-induced air pollution process as analyzed from Mie LiDAR data

A severe air pollution event in the Xianlin District of Nanjing City, China during 23-24 December 2012 was analyzed in terms of aerosol extinction coefficient and AOT retrieved from Mie scattering LiDAR data, in conjunction with in situ particulate concentrations measured near the Earth's surface, and the Weather Research Forecast-derived meteorological conditions. Comprehensive analyses of temperature, humidity, wind direction and velocity, and barometric pressure led to the conclusion that this pollution event was caused by advection inversion. In the absence of temperature inversion, the atmosphere at a height of 0.15 km has a relatively large extinction coefficient. In situ measured particulates exhibited a very large diurnal range. However, under the influence of turbulences, AOT was rather stable with a value <0.2 at an altitude below 0.8 km. Advection inversion appeared at 9:00 AM on 24 December, and did not dissipate until 22:00 PM. This temperature inversion, to some degree, inhibited the dispersion of near-surface particulates. Affected by this temperature inversion, the atmospheric extinction coefficient near the surface became noticeably larger. Near-surface particulates hardly varied at a concentration around 0.2mg/m(3). AOT at an altitude below 0.8 km rose to 0.31.