Features Exploration from Datasets Vision in Air Quality Prediction Domain

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data

Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas

Accurate long-term forecasts of PM2.5 pollution are essential to mitigating health risks and formulating pollutant control strategies for decision-makers in China. In this study, an objective identification and forecast method for PM2.5 pollution (OIF-PM2.5) is developed based on medium- and long-term ensemble forecasts of PM2.5 in Beijing-Tianjin-Hebei region and its surrounding areas. The results show that the observed PM2.5 pollution ratio increases with the aggravating PM2.5 pollution. For example, the ratio of meteorological stations with heavy pollution is 4.4 times that of light pollution and 3.9 times that of moderate pollution. In addition, the correlation coefficients between observations and forecasts are above 0.60 for all forecast leading times. Statistical results show that the average accuracy for forecasts with the leading times of 1–3 days, 4–7 days, and 8–15 days are 74.1%, 81.3%, and 72.9% respectively, indicating that the OIF-PM2.5 method has a high reliability in forecasts with the leading times of 1–15 days. The OIF-PM2.5 method is further applied in a severe PM2.5 pollution episode in the December of 2021, and the average forecast precision in forecasts with the leading times of 6–8 days reaches as high as 100%, showing a certain reference value for PM2.5 forecasts

Causal impacts of transport interventions on air quality

The transport sector is one of the main sources of air pollution emissions, particularly for carbon monoxide, nitrogen oxides, and particulate matter. Evaluating the effectiveness of transport interventions on improving air quality is essential to informing future policy. However, a comparison of air quality observations before and after an intervention can be biased by various factors, such as weather conditions and seasonality effects. Causal inference methods generally have advantages in intervention evaluation in terms of data requirements, model building, and the interpretation of effect estimates. Causality goes beyond statistical association in the sense that it seeks to measure the net effect of an intervention on an outcome through all possible pathways directing from the intervention to the outcome. Causal inference methods have been applied to address the same question, however, the important confounders (such as weather conditions) are commonly controlled for by including variables in the causal inference model and assuming a parametric relationship. The thesis focuses on understanding the causal impacts of transport interventions on air quality. A novel ex-post policy evaluation framework, combining meteorological normalisation, change point detection, and causal inferencing, is proposed to overcome the limitations of previous approaches, and it is applied to three distinct transport interventions: improving public transport supply (Jubilee Line Extension), tightening road traffic emission standards (London Ultra Low Emission Zone), and restricting both transport activities and supply (COVID-19 lockdown). The Jubilee Line extension led to only small (< 1%) or insignificant changes in air pollution on average in London. The Ultra Low Emission Zone showed an average reduction of less than 3% for NO2 concentrations and insignificant effects on O3 and PM2.5 concentrations. The lockdown reduced the NO2 concentrations in London by less than 12% on average, and it had an insignificant effect on O3, PM10, and PM2.5. Therefore, the empirical results of the thesis consistently highlight the necessity of a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional, and national government in order to achieve long-term improvements in air quality in cities.Open Acces

Urban air pollution modelling with machine learning using fixed and mobile sensors

Detailed air quality (AQ) information is crucial for sustainable urban management, and many regions in the world have built static AQ monitoring networks to provide AQ information. However, they can only monitor the region-level AQ conditions or sparse point-based air pollutant measurements, but cannot capture the urban dynamics with high-resolution spatio-temporal variations over the region. Without pollution details, citizens will not be able to make fully informed decisions when choosing their everyday outdoor routes or activities, and policy-makers can only make macroscopic regulating decisions on controlling pollution triggering factors and emission sources. An increasing research effort has been paid on mobile and ubiquitous sampling campaigns as they are deemed the more economically and operationally feasible methods to collect urban AQ data with high spatio-temporal resolution. The current research proposes a Machine Learning based AQ Inference (Deep AQ) framework from data-driven perspective, consisting of data pre-processing, feature extraction and transformation, and pixelwise (grid-level) AQ inference. The Deep AQ framework is adaptable to integrate AQ measurements from the fixed monitoring sites (temporally dense but spatially sparse), and mobile low-cost sensors (temporally sparse but spatially dense). While instantaneous pollutant concentration varies in the micro-environment, this research samples representative values in each grid-cell-unit and achieves AQ inference at 1 km \times 1 km pixelwise scale. This research explores the predictive power of the Deep AQ framework based on samples from only 40 fixed monitoring sites in Chengdu, China (4,900 {\mathrm{km}}^\mathrm{2}, 26 April - 12 June 2019) and collaborative sampling from 28 fixed monitoring sites and 15 low-cost sensors equipped with taxis deployed in Beijing, China (3,025 {\mathrm{km}}^\mathrm{2}, 19 June - 16 July 2018). The proposed Deep AQ framework is capable of producing high-resolution (1 km \times 1 km, hourly) pixelwise AQ inference based on multi-source AQ samples (fixed or mobile) and urban features (land use, population, traffic, and meteorological information, etc.). This research has achieved high-resolution (1 km \times 1 km, hourly) AQ inference (Chengdu: less than 1% spatio-temporal coverage; Beijing: less than 5% spatio-temporal coverage) with reasonable and satisfactory accuracy by the proposed methods in urban cases (Chengdu: SMAPE \mathrm{<} 20%; Beijing: SMAPE \mathrm{<} 15%). Detailed outcomes and main conclusions are provided in this thesis on the aspects of fixed and mobile sensing, spatio-temporal coverage and density, and the relative importance of urban features. Outcomes from this research facilitate to provide a scientific and detailed health impact assessment framework for exposure analysis and inform policy-makers with data driven evidence for sustainable urban management.Open Acces

Impacts of unconventional oil and gas development on atmospheric aerosol particles

2017 Summer.Includes bibliographical references.Rising demands for global energy production and shifts in the economics of fossil fuel production have recently driven rapid increases in unconventional oil and gas drilling operations in the United States. Limited field measurements of atmospheric aerosol particles have been conducted to understand the impacts of unconventional oil and gas extraction on air quality. These impacts can include emissions of greenhouse gases, the release of volatile organic compounds that can be hazardous and precursors to tropospheric ozone formation, and increases in atmospheric aerosol particles. Aerosol particles can also contribute to climate change, degrade visibility and negatively impact human health and the environment. Aerosol formation can result from a variety of activities associated with oil and gas drilling operations, including emission of particles and/or particle precursors such as nitrogen oxides from on-site power generation, evaporation or leaking of fracking fluids or the produced fuel, flaring, the generation of road dust, and increases in traffic and other anthropogenic emissions associated with growing populations near drilling locations. The work presented here details how activities associated with unconventional oil and gas extraction impact aerosol particle characteristics, sources, and formation in remote regions. An air quality field study was conducted in the Bakken formation region during a period of rapid growth in oil production by unconventional techniques over two winters in 2013 and 2014. The location and time of year were chosen because long term IMPROVE network monitoring records show an increasing trend in particulate nitrate concentrations and haze in the Bakken region during the winter, strongly contrasting with sharp decreases observed across most of the U.S. The comprehensive suite of instrumentation deployed for the Bakken Air Quality Study (BAQS) included measurements of aerosol concentrations, composition, and scattering, gaseous precursors important for aerosol formation, volatile organic compounds, and meteorology. Regional measurements of inorganic aerosol composition were collected, with average concentrations of total inorganic PM2.5 between 4.78 – 6.77 µg m-3 and 1.99 – 2.52 µg m-3 for all sampling sites during the 2013 and 2014 study periods, respectively. The maximum inorganic PM2.5 concentration observed was 21.3 µg m-3 for a 48 hour filter sample collected at Fort Union National Historical Site, a site located within a dense area of oil wells. Organic aerosol measurements obtained during the second study at the north unit of Theodore Roosevelt National Park (THRO-N) featured an average concentration of 1.1 ± 0.7 µg m-3. While oil production increased from 2013 to 2014, the lower PM2.5 in 2014 can be explained by the meteorological differences. During the first study, increased snow cover, atmospheric stability, solar illumination, and differences in the dominant wind direction contributed to higher PM2.5. The enhanced concentrations of inorganic PM2.5 measured in the Bakken region were tied to regional oil and gas development. Elevated concentrations of PM2.5 were observed during periods of air mass stagnation and recirculation and were associated with VOC emissions aged less than a day, both indicating a predominant influence from local emissions. High PM2.5 concentrations occurred when low i-/n-pentane VOC ratios were observed, indicating strong contributions from oil and gas operations. The hourly measurements of gas and aerosol species in an extremely cold environment also provided a unique data set to investigate how well thermodynamic aerosol models represent the partitioning of ammonium nitrate. In general, during the coldest temperatures, the models overpredicted the formation of particulate nitrate. The formation of additional PM2.5 in this region is more sensitive to availability of N(-III) species during the coldest periods but increasingly sensitive to available N(V) when temperatures are relatively warmer and ammonia availability increases. These measurements and modeling results show that continued growth of oil and gas drilling operations in remote areas such as the Bakken region could lead to increased PM2.5 and impact haze formation in nearby federally protected lands

Human activity and climate variability project: annual report 2002.

This project aims to utilise nuclear techniques to investigate evidence of human activity and climate variability in the Asia Australasian regions. It was originally designed to run over three years, commencing July 1999, with three parallel research tasks: Task 1: Past -- Natural archives of human activity and climate variability; Task 2: Present -- Characterisation of the global atmosphere using radon and fine particles; Task 3: Future -- Climate modelling: evaluation and improvement; Main project objectives -- To determine what proportions of changes in natural archives are due to human activity and climate variability; -- To contribute to the understanding of the impact of human induced and natural aerosols in the East Asian region on climate through analysis and sourcing of fine particles and characterisation of air samples using radon concentrations; -- To contribute to the improvement of land surface parameterisation schemes and investigate the potential to use isotopes to improve global climate models and thus improve our understanding of future climate. Significant project outcomes -- An improved understanding of natural and anthropogenic factors influencing change in our environment; -- A better understanding of the role of aerosols in climate forcing in the Asian region, leading to improved ability to predict climate change; -- An improved understanding of long term changes in the concentrations of trace species in the atmosphere on a regional and a global basis and their use in model evaluation; -- Improved understanding of the impact of different land-surface schemes on simulations by atmospheric models. The next two years of the project Our new and extended projects efforts include: 1) Aligning ourselves with the recently developed mission of the IGBP/PAGES research program 'Human Interactions on Terrestrial Ecosystems' and co-ordinating the Australasian research effort. Further research will focus on: (1) How widespread and reliable are evidence of major climatic events, such as storms and El Nino/La Nina cycles, in natural archives? This would require more natural archives to be examined from northern Australia and also records to be obtained from southern Australia. (2) The spatial extent of mining related pollutants, in the form of aerosol particles, which is of importance to managing the waste in the future. A combination of aerosol and archival studies will address this issue. In Summary: To achieve these extended goals we successfully gained another two years of further support for our project

Simulating southwestern U.S. desert dust influences on severe, tornadic storms

2012 Spring.Includes bibliographical references.In this study, three-dimensional numerical simulations were performed using the Regional Atmospheric Modeling System (RAMS) model to investigate possible southwestern U.S. desert dust impacts on severe, tornadic storms. Initially, two sets of simulations were conducted for an idealized supercell thunderstorm. In the first set, two numerical simulations were performed to assess the impacts of increased aerosol concentrations acting as cloud condensation nuclei (CCN) and giant CCN (GCCN). Initial profiles of CCN and GCCN concentrations were set to represent "clean" continental and aerosol-polluted environments, respectively. With a reduction in warm- and cold-rain processes, the polluted environment produced a longer-lived supercell with a well-defined rear flank downdraft (RFD) and relatively weak forward flank downdraft (FFD) that produced weak evaporative cooling, a weak cold-pool, and an EF-1 tornado. The clean environment produced no tornado and was less favorable for tornadogenesis. In the second ensemble, aerosol microphysical effects were put into context with those of convective available potential energy (CAPE) and low-level moisture. Simulations initialized with greater low-level moisture and higher CAPE produced significantly stronger precipitation, which resulted in greater evaporation and associated cooling, thus producing stronger cold-pools at the surface associated with both the forward- and rear-flank downdrafts. Simulations initialized with higher CCN concentrations resulted in reduced warm rain and more supercooled water aloft, creating larger anvils with less ice mass available for precipitation. These simulated supercells underwent less evaporative cooling within downdrafts and produced weaker cold-pools compared to the lower CCN simulations. Tornadogenesis was related to the size, strength, and location of the FFD- and RFD-based cold-pools. The combined influence of low-level moisture and CAPE played a considerably larger role on tornadogenesis compared to aerosol impacts. However, the aerosol effect was still evident. In both idealized model ensembles, the strongest, longest-lived tornado-like vortices were associated with warmer and weaker cold-pools, higher CAPE, and less negative buoyancy in the near-vortex environment compared to those storms that produced shorter-lived, weaker vortices. A final set of nested grid simulations were performed to evaluate dust indirect microphysical and direct radiative impacts on a severe storms outbreak that occurred during 15-16 April 2003 in Texas and Oklahoma. In one simulation, neither dust microphysical nor radiative effects were included (CTL). In a second simulation, only dust radiative effects were considered (RAD). In a third simulation, both dust radiative and indirect microphysical effects were simulated (DST), where dust was allowed to serve as CCN, GCCN, and ice nuclei (IN). Fine mode dust serving as CCN reduced warm rain formation in the DST simulation. Thus, cloud droplets were transported into the mixed phase region, enhancing freezing, aggregation, and graupel and hail production. However, graupel and hail were of smaller sizes in the DST simulation due to reduced riming efficiencies. Dust particles serving as GCCN and IN played secondary roles, as these impacts were offset by other processes. The DST simulation yielded the lowest rainfall rates and accumulated precipitation, as much of the total water mass within the convective cells were in the form of aggregates and small graupel particles that were transported into the anvil region rather than falling as precipitation. The combined effects of warm rain efficiency, ice production, and hydrometeor size controlled the evolution of cold-pools and storm structure. The RAD and CTL simulations produced widespread cold-pools, which hindered the formation of long-lived supercells relative to the DST simulation. The DST convective line was associated with reduced rainfall and multiple long-lived supercells. Comparisons between the RAD and CTL simulations revealed that dust radiative influences played an important role in convective initiation. The increased absorption of solar radiation within the dust plume in the RAD simulation warmed the dust layer over time, which reduced the amount of radiation that reached the surface, resulting in slight cooling at the surface and increased atmospheric stability within the lowest 2 km. Dew points at low levels were slightly lower in the RAD simulation, due to reduced surface water vapor fluxes (latent heat fluxes) below the dust plume. With the presence of a stronger capping inversion but more available low-level moisture, the CTL simulation initially produced more widespread convection and precipitation, while the RAD simulation produced the strongest convective cores, including a long-lived supercell. The results from all three sets of simulations suggest that dust indirect microphysical and direct radiative impacts on severe convection may at times greatly influence the development of severe storms. In this study, dust often increased the potential for tornadogenesis. Additional modeling studies at horizontal grid spacing &le;100 m are needed in order to address the robustness of these results and better isolate potential dust influences on severe storms and tornadogenesis