An historical experiment: Los Angeles smog evolution observed by blimp

Phytoremediation as a potential technique for vehicle hazardous pollutants around highways

With the synchronous development of highway construction and the urban economy, automobiles have entered thousands of households as essential means of transportation. This paper reviews the latest research progress in using phytoremediation technology to remediate the environmental pollution caused by automobile exhaust in recent years, including the prospects for stereoscopic forestry. Currently, most automobiles on the global market are internal combustion vehicles using fossil energy sources as the primary fuel, such as gasoline, diesel, and liquid or compressed natural gas. The composition of vehicle exhaust is relatively complex. When it enters the atmosphere, it is prone to a series of chemical reactions to generate various secondary pollutants, which are very harmful to human beings, plants, animals, and the eco-environment. Despite improving the automobile fuel quality and installing exhaust gas purification devices, helping to reduce air pollution, the treatment costs of these approaches are expensive and cannot achieve zero emissions of automobile exhaust pollutants. The purification of vehicle exhaust by plants is a crucial way to remediate the environmental pollution caused by automobile exhaust and improve the environment along the highway by utilizing the ecosystem's self-regulating ability. Therefore, it has become a global trend to use phytoremediation technology to restore the automobile exhaust pollution. Now, there is no scientific report or systematic review about how plants absorb vehicle pollutants. The screening and configuration of suitable plant species is the most crucial aspect of successful phytoremediation. The mechanisms of plant adsorption, metabolism, and detoxification are reviewed in this paper to address the problem of automobile exhaust pollution.

The Potential Impact of Smog Spell on Humans' Health Amid COVID-19 Rages

Rapid and unchecked industrialization and the combustion of fossil fuels have engendered a state of fear in urban settlements. Smog is a visible form of air pollution that arises due to the over-emissions of some primary pollutants like volatile organic compounds (VOCs), hydrocarbons, SO₂, NO, and NO₂ which further react in the atmosphere and give rise to toxic and carcinogenic secondary smog components. Smog reduces the visibility on roads and results in road accidents and cancellation of flights. Uptake of primary and secondary pollutants of smog is responsible for several deleterious diseases of which respiratory disorders, cardiovascular dysfunction, neurological disorders, and cancer are discussed here. Children and pregnant women are more prone to the hazards of smog. The worsening menace of smog on one hand and occurrence of pandemic i.e., COVID-19 on the other may increase the mortality rate. But the implementation of lockdown during pandemics has favored the atmosphere in some ways, which will be highlighted in the article. On the whole, the focus of this article will be on the dubious relationship between smog and coronavirus.

Development of urban air monitoring with high spatial resolution using mobile vehicle sensors

Traditionally, the equipment used to measure air pollution is expensive and placed around cities or in mobile laboratories. It might only represent a certain area and not the entire city due to the locations and limited number of monitoring stations. Nowadays, a mobile sensing is becoming an alternative option to monitor air quality in urban environment due to its ease of use, high flexibility, and low price. This paper develops a vehicular-based mobile monitoring system for real time air quality sensing and visualization across large cities with high spatial resolution. The commercially available low-cost CO, NO[Formula: see text], NH[Formula: see text] O[Formula: see text], CH[Formula: see text], SO[Formula: see text], PM[Formula: see text], temperature and humidity sensors along with the microcontroller and GPS were integrated in a sensing device installed on the roof of taxi and sport utility vehicle (SUV). The developed device was calibrated through a reference monitoring station and validated through field measurement. We first split the entire city with a uniform grid discretization. We then propose a data processing methodology based on machine learning algorithms for generating 250 representative data set from 286 million data which is collected using the vehicular based mobile sensors. Next we present the representativeness of the data set by comparison of stationary data and mobile data. We also describe the analytical results and spatial distribution with high spatial resolution throughout the city. In addition, the collected mobile sensor data is also used to show that the significant differences and spatial variability in mean levels per street. Finally, we conclude that the proposed mobile monitoring system using high spatial resolution can effectively map the air quality in metropolitan environment and provide detail about the spatial variability that cannot be done with stationary monitoring systems.

The haze extreme co-movements in Beijing-Tianjin-Hebei region and its extreme dependence pattern recognitions

Extreme haze was often observed at many locations in Beijing-Tianjin-Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014-2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing-Tianjin-Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze's appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

A study on the relationship between air pollution and pulmonary tuberculosis based on the general additive model in Wulumuqi, China

OBJECTIVE

This study aimed to explore the impact of atmospheric pollutants on the incidence of tuberculosis (TB), and provide new ideas for the prevention and control of TB in the future.

METHODS

It explored the relationship between air pollutants and meteorological factors, as well as between air pollutants and heating through Spearman correlation analysis and rank sum test. Additionally, it analyzed the relationship between air pollutants and TB incidence using the general additive model. Statistical analysis results at the p<0.05 level were considered significant.

RESULTS

Three months after exposure to air pollutants (PM_2.5, SO₂, NO₂, and CO) TB incidence increased. However, TB incidence increased 9 months after exposure to PM₁₀. The single pollutant model showed when concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ increased by 1μg/m³ (or 1mg/m³), the number of TB cases increased by 0.09%, 0.08%, 0.58%, 0.42%, 6.9%, and 0.57%, respectively. The optimal multi-pollutant model was a two-factor model (PM₁₀+NO₂).

CONCLUSION

Air pollutants including PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ increased the risk of TB. Few studies have been conducted in this area of research, especially regarding the mechanism. The results of this study should contribute to the understanding of TB incidence and prompt additional research.

Atmospheric Chemistry in a Box or a Bag

Environmental chambers have proven to be essential for atmospheric photochemistry research. This historical perspective summarizes chamber research characterizing smog. Experiments with volatile organic compounds (VOCs)-nitrogen oxides (NOx) have characterized O3 and aerosol chemistry. These led to the creation and evaluation of complex reaction mechanisms adopted for various applications. Gas-phase photochemistry was initiated and developed using chamber studies. Post-1950s study of photochemical aerosols began using smog chambers. Much of the knowledge about the chemistry of secondary organic aerosols (SOA) derives from chamber studies complemented with specially designed atmospheric studies. Two major findings emerge from post-1990s SOA experiments: (1) photochemical SOAs hypothetically involve hydrocarbons and oxygenates with carbon numbers of 2, and (2) SOA evolves via more than one generation of reactions as condensed material exchanges with the vapor phase during “aging”. These elements combine with multiphase chemistry to yield mechanisms for aerosols. Smog chambers, like all simulators, are limited representations of the atmosphere. Translation to the atmosphere is complicated by constraints in reaction times, container interactions, influence of precursor injections, and background species. Interpretation of kinetics requires integration into atmospheric models addressing the combined effects of precursor emissions, surface exchange, hydrometeor interactions, air motion and sunlight.