Do heat waves worsen air quality? A 21-year observational study in Seoul, South Korea.

Heat waves are generally known to deteriorate air quality. However, the impacts of heat waves on air quality can substantially vary depending on the characteristics of heat waves. In this study, we examine air quality changes in Seoul during heat waves and their associations with large-scale atmospheric patterns. For this, air quality data from 25 stations and meteorological data from 23 weather stations and reanalysis datasets during July and August of 2001-2021 are used. Under heat waves, the mean daily PM10, NO2, and CO concentrations decrease by 7.9 %, 6.1 %, and 4.6 %, respectively, whereas the mean daily PM2.5, O3, and SO2 concentrations increase by 4.1 %, 17.2 %, and 2.9 %, respectively. The atmospheric circulation under heat waves is less favorable for long-range transport of air pollutants to Seoul. The PM2.5/PM10 ratio increases under heat waves, indicating that the secondary formation of aerosols becomes more important under heat waves. 37 % of the heat wave days are accompanied by severe O3 pollution exceeding the O3 concentration standard in South Korea. There is a significant variability of air quality in Seoul within heat waves. The heat wave days with higher concentrations of PM2.5, PM10, O3, NO2, and CO than their non-heat wave means exhibit a prominent difference in large-scale atmospheric pattern from the heat wave days with lower concentrations. This difference is characterized by a zonal wave-like pattern of geopotential height, which is similar to the circumglobal teleconnection pattern known as one of the major patterns for heat waves in South Korea. This zonal wave-like pattern produces more stagnant conditions over Seoul.

Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh.

Dry days at varied scale are an important topic in climate discussions. Prolonged dry days define a dry period. Dry days with a specific rainfall threshold may visualize a climate scenario of a locality. The variation of monthly dry days from station to station could be correlated with several climatic factors. This study suggests a novel approach for predicting monthly dry days (MDD) of six target stations using different machine learning (ML) algorithms in Bangladesh. Several rainfall thresholds were used to prepare the datasets of monthly dry days (MDD) and monthly wet days (MWD). A group of ML algorithms, like Bagged Trees (BT), Exponential Gaussian Process Regression (EGPR), Matern Gaussian Process Regression (MGPR), Linear Support Vector Machine (LSVM), Fine Trees (FT) and Linear Regression (LR) were evaluated on building a competitive prediction model of MDD. In validation of the study, EGPR-based models were able to better capture the monthly dry days (MDD) over Bangladesh compared to those by MGPR, LSVM, BT, LR and FT-based models. When MDD were the predictors for all six target stations, EGPR produced highest mean R2 of 0.91 (min. 0.89 and max. 0.92) with a least mean RMSE of 2.14 (min. 1.78 and max. 2.69) compared to other models. An explicit evaluation of the ML algorithms using one-year lead time approach demonstrated that BT and EGPR were the most result-oriented algorithms (R2 = 0.78 for both models). However, having a least RMSE, EGPR was chosen as the best model in one year lead time. The dataset of monthly dry-wet days was the best predictor in the lead-time approach. In addition, sensitivity analysis demonstrated sensitivity of each station on the prediction of MDD of target stations. Monte Carlo simulation was introduced to assess the robustness of the developed models. EGPR model declared its robustness up to certain limit of randomness on the testing data. The output of this study can be referred to the agricultural sector to mitigate the impacts of dry spells on agriculture.

Increasing model vertical resolution may not necessarily lead to improved atmospheric predictability.

The widely accepted existence of an inherent limit of atmospheric predictability is usually attributed to weather's sensitive dependence on initial conditions. This signature feature of chaos was first discovered in the Lorenz system, initially derived as a simplified model of thermal convection. In a recent study of a high-dimensional generalization of the Lorenz system, it was reported that the predictability of its chaotic solutions exhibits a non-monotonic dimensional dependence. Since raising the dimension of the Lorenz system is analogous to refining the model vertical resolution when viewed as a thermal convection model, it is questioned whether this non-monotonicity is also found in numerical weather prediction models. Predictability in the sense of sensitive dependence on initial conditions can be measured based on deviation time, that is, the time of threshold-exceeding deviations between the solutions with minute differences in initial conditions. Through ensemble experiments involving both the high-dimensional generalizations of the Lorenz system and real-case simulations by a numerical weather prediction model, this study demonstrates that predictability can depend non-monotonically on model vertical resolution. Further analysis shows that the spatial distribution of deviation time strongly contributes to this non-monotonicity. It is suggested that chaos, or sensitive dependence on initial conditions, leads to non-monotonic dependence on model vertical resolution of deviation time and, by extension, atmospheric predictability.

Systematic comparison between the generalized Lorenz equations and DNS in the two-dimensional Rayleigh-Bénard convection.

The classic Lorenz equations were originally derived from the two-dimensional Rayleigh-Bénard convection system considering an idealized case with the lowest order of harmonics. Although the low-order Lorenz equations have traditionally served as a minimal model for chaotic and intermittent atmospheric motions, even the dynamics of the two-dimensional Rayleigh-Bénard convection system is not fully represented by the Lorenz equations, and such differences have yet to be clearly identified in a systematic manner. In this paper, the convection problem is revisited through an investigation of various dynamical behaviors exhibited by a two-dimensional direct numerical simulation (DNS) and the generalized expansion of the Lorenz equations (GELE) derived by considering additional higher-order harmonics in the spectral expansions of periodic solutions. Notably, GELE allows us to understand how nonlinear interactions among high-order modes alter the dynamical features of the Lorenz equations including fixed points, chaotic attractors, and periodic solutions. It is verified that numerical solutions of the DNS can be recovered from the solutions of GELE when we consider the system with sufficiently high-order harmonics. At the lowest order, the classic Lorenz equations are recovered from GELE. Unlike in the Lorenz equations, we observe limit tori, which are the multi-dimensional analog of limit cycles, in the solutions of the DNS and GELE at high orders. Initial condition dependency in the DNS and Lorenz equations is also discussed.

Inter- and intra-city comparisons of PM2.5 concentration changes under COVID-19 social distancing in seven major cities of South Korea.

The COVID-19 pandemic has prompted governments around the world to impose mitigation strategies of unprecedented scales, typically involving some form of restrictions on social activities and transportation. The South Korean government has been recommending a collection of guidelines now known as social distancing, leading to reduced human activities. This study analyzes changes in the concentrations of fine particulate matter (PM2.5) during the 30-day periods before and since the start of social distancing on 29 February 2020 using measurement data from air quality monitoring stations at various locations of the seven major cities of South Korea, namely, Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, and Ulsan. All seven cities experienced decreased levels of PM2.5 concentration by up to 25% and smaller fluctuations during the period of social distancing. Inter-city comparisons show that the PM2.5 concentration changes are positively correlated with the city-wide PM2.5 emission fractions for mobile sources and negatively correlated with the city-wide PM2.5 emission fractions for combustion and industrial process sources. In addition, the meteorological influences favorable for transboundary pollutant transport have weakened during the period under COVID-19 social distancing. Intra-city comparisons show that decreases in the intra-city variability of PM2.5 concentration were larger in coastal cities than in inland cities. Comparisons between the inter- and intra-city variabilities in the PM2.5 concentration changes under social distancing highlight the importance of taking into account intra-city variabilities in addition to inter-city variabilities.

Water use efficiency in terrestrial ecosystem over East Asia: Effects of climate regimes and land cover types.

Water use efficiency (WUE) is an environmental factor to account for the metabolism of terrestrial ecosystems using various climate systems and vegetation types. It is estimated by the ratio of gross primary productivity (GPP) to evapotranspiration (ET), the largest carbon and water fluxes with respect to plant respiration. In this study, the WUE was calculated using GPP and ET from the community land model version 4.0 (CLM4.0), inclusive of the prognostic carbon-nitrogen model in the community earth system model (CESM). The estimated WUE in East Asia was analyzed for climate zones, land cover types, and water- and energy-limited zones, with aridity index (AI). Spatial variations from 2001 to 2015 in annual WUE gradually increased as latitude decreased, though small year-to-year differences appeared between monthly GPP and ET. Monthly WUE was lower in summer than fall because the water loss rate in summer was higher than the carbon assimilation increase. The WUE under arid conditions (AI<0.5) was lower than under humid conditions. The GPP, ET, and WUE were higher in the forest, savannas, cropland, and permanent wetland with dense vegetation or abundant water resources than in other land cover types. The WUE was lower in water-limited zones than in energy-limited zones due to the low amount of water to use for the physical processes of GPP and ET. Based on this study, we identified general spatial and temporal variations of carbon fluxes in East Asia with various climate zones and land cover types.

Air Quality Change in Seoul, South Korea under COVID-19 Social Distancing: Focusing on PM2.5.

Seoul, the most populous city in South Korea, has been practicing social distancing to slow down the spread of coronavirus disease 2019 (COVID-19). Fine particulate matter (PM2.5) and other air pollutants measured in Seoul over the two 30 day periods before and after the start of social distancing are analyzed to assess the change in air quality during the period of social distancing. The 30 day mean PM2.5 concentration decreased by 10.4% in 2020, which is contrasted with an average increase of 23.7% over the corresponding periods in the previous 5 years. The PM2.5 concentration decrease was city-wide and more prominent during daytime than at nighttime. The concentrations of carbon monoxide (CO) and nitrogen dioxide (NO2) decreased by 16.9% and 16.4%, respectively. These results show that social distancing, a weaker forcing toward reduced human activity than a strict lockdown, can help lower pollutant emissions. At the same time, synoptic conditions and the decrease in aerosol optical depth over the regions to the west of Seoul support that the change in Seoul's air quality during the COVID-19 social distancing can be interpreted as having been affected by reductions in the long-range transport of air pollutants as well as local emission reductions.

Impact of Biophysical Mechanisms on Urban Heat Island Associated with Climate Variation and Urban Morphology.

The rapid development of urban areas can potentially alter hydro-meteorological fluxes and lead to the Urban Heat Island (UHI) phenomenon. In this study, UHI intensity and its driving factors were estimated using the Community Land Model (CLM) in cities of Tokyo, Phoenix, Bandung, and Quito, with different landscapes and climates, as a step in risk assessment of urbanization phenomena. The UHI magnitude increased along with the ratio of the height to width (H/W) of urban canyons in cities with the same latitude, especially during the daytime, when Quito (Tokyo) had a higher UHI than Bandung (Phoenix). El Niño-Southern Oscillation (ENSO) events, such as El Niño and La Niña, contributed to UHI variability, during which the cities in the western (eastern) part of Pacific Ocean experienced a higher UHI during El Niño (La Niña). The UHI differences from total biophysical drivers between these events were highest in Tokyo during the daytime as a result of convection process, and in Phoenix during the nighttime due to the hot arid climate of the city. Our results suggest the need to consider climate variation beyond local site characteristics when mitigating heat stress and making decisions regarding urban development.

A physically extended Lorenz system.

The Lorenz system is a simplified model of Rayleigh-Bénard convection, a thermally driven fluid convection between two parallel plates. Two additional physical ingredients are considered in the governing equations, namely, rotation of the model frame and the presence of a density-affecting scalar in the fluid, in order to derive a six-dimensional nonlinear ordinary differential equation system. Since the new system is an extension of the original three-dimensional Lorenz system, the behavior of the new system is compared with that of the old system. Clear shifts of notable bifurcation points in the thermal Rayleigh parameter space are seen in association with the extension of the Lorenz system, and the range of thermal Rayleigh parameters within which chaotic, periodic, and intermittent solutions appear gets elongated under a greater influence of the newly introduced parameters. When considered separately, the effects of scalar and rotation manifest differently in the numerical solutions; while an increase in the rotational parameter sharply neutralizes chaos and instability, an increase in a scalar-related parameter leads to the rise of a new type of chaotic attractor. The new six-dimensional system is found to self-synchronize, and surprisingly, the transfer of solutions to only one of the variables is needed for self-synchronization to occur.

Relationship between rooftop and on-road concentrations of traffic-related pollutants in a busy street canyon: Ambient wind effects.

Rooftop and on-road measurements of O3, NO2, NOx, and CO concentrations were conducted to investigate the relationship between rooftop and on-road concentrations in a busy and shallow street canyon with an aspect ratio of ∼0.3 in Seoul, Republic of Korea, from 15 April to 1 May 2014. The median road-to-roof concentration ratios, correlation coefficients between rooftop and on-road concentrations, and temporal variations of rooftop and on-road concentrations are analyzed according to the rooftop wind directions which are two cross-canyon and two along-canyon directions. The analysis results indicate that the relationship is strong when the rooftop is situated on the downwind side rather than on the upwind side. Relative to the cross-canyon wind directions, one of the along-canyon wind directions can more enhance the relationship. A conceptual framework is proposed to explain the effect of ambient wind direction on the relationship between rooftop and on-road concentrations in a street canyon.