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Negligible losses were observed for many atmospheric trace species after traversing the PFA Teflon tubing that was hundreds of meters in length.

Fireworks are widely used around the world and can cause severe air pollution over a short period of time. Many efforts have been carried out worldwide to reduce the level of firework-generated air pollution, such as limiting the use of fireworks and developing environmentally friendly fireworks. Research has suggested that the use of environmentally friendly charges in fireworks can reduce emissions, although their impact on ambient air quality has not been quantified. Here, we used a chemical transport model to study the benefits of environmentally friendly fireworks in reducing ambient PM2.5 based on a detailed estimation of emissions from fireworks and dense observations available for simulation validation. Our results showed that environmentally friendly fireworks can reduce ambient PM2.5 by ∼50% (in the range of 15-65% with a "central" value of 35% considering uncertainties) compared with traditional fireworks. However, due to a large number of fireworks used, the air quality still significantly deteriorated, and the effect of using twice the amount of environmentally friendly fireworks on air quality would be comparable to the use of traditional fireworks. Our results indicate that environmentally friendly fireworks are not actually "green". To make them green, the total number of fireworks used at one time must be strictly restricted.

The complexity and heterogeneity of urban land surfaces result in inconsistencies in near-surface winds, which in turn influence the diffusion and dispersion of air pollutants. In this study, we classified urban surface wind fields, quantified their steadiness, duration, and influence on air quality using hourly wind observations from 50 meteorological stations, as well as hourly PM

With the increase economic growth and high energy consumption in Shenzhen, pollutant emission increases gradually which leads to air pollution and affect public health. In order to undertake effective measures for preventing and controlling air pollution, it is essential to understand the transmission characteristics of PM2.5 and identify the cause of air pollution in Shenzhen. Based on LIDAR data of Shenzhen, China, the transmission characteristics of PM2.5 across Shenzhen's borders in the winter of 2017 (from December 1, 2017 to February 28, 2018) were analyzed using a four-dimensional flux method. In addition, the sources of PM2.5 in Shenzhen from December 8 to 10, 2017 were analyzed. The results revealed that the East Asian winter monsoon was primarily responsible for the transmission of PM2.5 in Shenzhen, from north to south, and its surrounding areas, and high PM2.5 flux was primarily located at the altitude range of 500–1,500 m. During the monsoon transition period, the surface wind directions changed frequently, which resulted in varying transmission directions of PM2.5 fluxes across Shenzhen. During the period from December 8 to10, PM2.5 was continuously transported to the urban areas of Shenzhen via its northern border due to the influence of strong northerly wind. The PM2.5 fluxes were positive over Songgang station (in the northwestern corner of Shenzhen) and Xiapi station (in the northeastern corner of Shenzhen) from December 8 to 9. The analysis revealed that, the PM2.5 source contribution from regional transmission (representing other land areas within the model domain) reached approximately 50%, whereas the local source contribution was only approximately 10% in Shenzhen.

Air pollutants have significant direct and indirect adverse effects on public health. To explore the relationship between air pollutants and meteorological conditions on the hospitalization for respiratory diseases, we collected a whole year of daily major air pollutants’ concentrations from Shenzhen city in 2013, including Particulate Matter (PM10, PM2.5), Nitrogen dioxide (NO2), Ozone (O3), Sulphur dioxide (SO2), and Carbon monoxide (CO). Meanwhile, we also gained meteorological data. This study collected 109,927 patients cases with diseases of the respiratory system from 98 hospitals. We investigated the influence of meteorological factors on air pollution by Spearman correlation analysis. Then, we tested the short-term correlation between significant air pollutants and respiratory diseases’ hospitalization by Distributed Lag Non-linear Model (DLNM). There was a significant negative correlation between the north wind and NO2 and a significant negative correlation between the south wind and six pollutants. Except for CO, other air pollutants were significantly correlated with the number of hospitalized patients during the lag period. Most of the pollutants reached maximum Relative Risk (RR) with a lag of five days. When the time lag was five days, the annual average of PM10, PM2.5, SO2, NO2, and O3 increased by 10%, and the risk of hospitalization for the respiratory system increased by 0.29%, 0.23%, 0.22%, 0.25%, and 0.22%, respectively. All the pollutants except CO impact the respiratory system’s hospitalization in a short period, and PM10 has the most significant impact. The results are helpful for pollution control from a public health perspective.