Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Identification of response regulation governing ozone formation based on influential factors using a random forest approach.

The pursuit of enhanced scientific, refined, and precise ozone and air quality control continues to pose significant challenges. Using data visualization techniques and random forest (RF) algorithms, the temporal distribution of atmospheric pollutants and the interrelationship between O3 concentration and its influential factors were investigated with one-year monitoring data in Deqing county in 2021. The local atmospheric conditions predominantly belonged to NOx-sensitive and transition zone. Extremely high O3 concentration were primarily observed when temperatures (T) exceeded 30 °C, with relative humidity (RH) ranging between 30 and 60 %. NO2, RH and T were identified as the top 3 important factors, and O3 concentration have stronger linearly relationship to RH and T, while stronger nonlinearly relationship to NO2. By employing an optimized RF model, controlling consistent mild and high reaction atmospheric conditions, the O3 concentration response to the change of individual influencing factors was acquired. The O3 concentration increased and then decreased in response to the increasing NO2 concentration, displaying a characteristic inflection point at 10 µg m-3. More reactive radicals produced at higher VOCs concentration and continuing NOx cycle at lower NO2 concentration, resulting in the acceleration in the direction of producing more O3. Therefore, the significant different O3 response to variation of VOCs and NOx concentration between mild and high reaction atmospheric conditions, as well as the existing of oxidant elevation should be considered in local air quality control. This study demonstrates the efficacy of ML methods in simulating nonlinear response of O3, supports the understanding of local O3 formation and quick guidance for precise local O3 pollution control and the related strategies.

Psoriasis de novo or exacerbation by PD-1 checkpoint inhibitors.

PD-1 (programmed Death-1) immune checkpoint inhibitors have provided significant benefits to tumor patients. However, a considerable proportion of the patients develop immune-related adverse events (irAEs), of which cutaneous irAEs (cirAEs, e.g., psoriasis) occur relatively early. This review provides an overview of the current progress in psoriasis de novo or exacerbation by PD-1 checkpoint inhibitors. It not only describes the relevant influencing factors but also theoretically analyzes the immunological mechanisms that lead to the onset or exacerbation of psoriasis. Finally, the authors present guidelines for the treatment of psoriasis de novo or exacerbation by PD-1 checkpoint inhibitors. The review is intended to assist dermatologists in the early recognition and effective individualized management of such cirAE, which is helpful to continue or adjust the tumor-targeted immunotherapy on the basis of ensuring the quality of life of tumor patients.

Psoriasis de novo or exacerbation by PD-1 checkpoint inhibitors

Abstract PD-1 (programmed Death-1) immune checkpoint inhibitors have provided significant benefits to tumor patients. However, a considerable proportion of the patients develop immune-related adverse events (irAEs), of which cutaneous irAEs (cirAEs, e.g., psoriasis) occur relatively early. This review provides an overview of the current progress in psoriasis de novo or exacerbation by PD-1 checkpoint inhibitors. It not only describes the relevant influencing factors but also theoretically analyzes the immunological mechanisms that lead to the onset or exacerbation of psoriasis. Finally, the authors present guidelines for the treatment of psoriasis de novo or exacerbation by PD-1 checkpoint inhibitors. The review is intended to assist dermatologists in the early recognition and effective individualized management of such cirAE, which is helpful to continue or adjust the tumor-targeted immunotherapy on the basis of ensuring the quality of life of tumor patients.

Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park.

Industrial parks have more complex O3 formation mechanisms due to a higher concentration and more dense emission of precursors. This study establishes an artificial neural network (ANN) model with good performance by expanding the moment and concentration changes of pollutants into general variables of meteorological factors and concentrations of pollutants. Finally, the O3 formation rules and concentration response to the changes of volatile organic compounds (VOCs) and nitrogen oxides (NOx) was explored. The results showed that the studied area belonged to the NOx-sensitive regime and the sensitivity was strongly affected by relative humidity (RH) and pressure (P). The concentration of O3 tends to decrease with a higher P, lower temperature (Temp), and medium to low RH when nitric oxide (NO) is added. Conversely, at medium P, high Temp, and high RH, the addition of nitrogen dioxide (NO2) leads to a larger decrease capacity in O3 concentration. More importantly, there is a local reachable maximum incremental reactivity (MIRL) at each certain VOCs concentration level which linearly increased with VOCs. The general maximum incremental reactivity (MIR) may lead to a significant overestimation of the attainable O3 concentration in NOx-sensitive regimes. The results can significantly support the local management strategies for O3 and the precursors control.