Twenty years of MODIS satellite data (2002-2022), TROPOMI glyoxal observations (2018-2022), and ground-based isoprene measurements were used to examine vegetation greenness (NDVI) and atmospheric glyoxal over Houston, Texas. Biogenically produced glyoxal grew by 51% between 2018 and 2022, despite a 2% per decade decrease in summer vegetation greenness and continued urbanization. Ambient mixing ratios of isoprene, the main biogenic glyoxal precursor, paradoxically dropped by 14% within the same time frame. Temperature (+0.68 degrees C/year), ozone (+28%), and photochemical oxidants all significantly increased over this time, according to analysis of concurrent environmental data. The results indicate that higher temperature-driven isoprene emissions (+35%) and accelerated photochemical oxidation (+10%) overcame the declining vegetation signal, resulting in net increases in atmospheric glyoxal. This suggests that Houston's remaining flora is experiencing temperature-driven changes in biogenic volatile organic compound (VOC) emissions per unit area, even while its greenness has reduced.

This study presents a comprehensive 2022 dataset of continuous in-situ measurements of delta(CO2)-C-13 and delta(CH4)-C-13 in Houston, Texas, USA complemented by targeted canister sampling to characterize key anthropogenic and biogenic emission sources. The analysis examines isotopic data of delta(CO2)-C-13 and delta(CH4)-C-13 to resolve source-specific emission patterns of CO2 and CH4 for Houston. Temporal analysis revealed distinct seasonal variability: delta(CO2)-C-13 was most depleted in winter, reflecting enhanced combustion-related CO2, whereas delta(CH4)-C-13 showed the most negative values in summer and fall, consistent with intensified microbial methanogenesis under warm, humid conditions. Background delta(CO2)-C-13 ranged from -11.8 parts per thousand to -13.2 parts per thousand depending on air mass origin, while delta(CH4)-C-13 varied between -47.2 parts per thousand and -50.7 parts per thousand, reflecting marine-continental transitions. Spatially, bivariate polar plots and isotopic mapping identified strong CH4 enhancements (>0.5 ppm) and highly depleted delta(CH4)-C-13 (-50.5 parts per thousand to -51 parts per thousand) over the McCarty landfill, indicating dominant microbial methane generation, further confirmed by canister measurements delta(CH4)-C-13 approximate to -60.3 parts per thousand. Estimated emissions from the McCarty Landfill, based on our isotopic mass-balance analysis, were similar to 2320 kg CH4 h(-1) +/- 71%, exceeding the EPA GHGRP inventory value (similar to 857 kg CH4 h(-1)) and moderately higher than the Carbon Mapper satellite-derived flux but within combined uncertainties (similar to 1427 kg CH4 h(-1) +/- 91%), highlighting that bottom-up inventories underestimate methane emissions from large urban landfills such as McCarty. Overall, the isotopic evidence demonstrates that integrating delta C-13 analyses data provides critical insights into source attribution and the relative roles of combustion, industrial, and microbial processes shaping Houston's CO2 and CH4 emission landscape.

This study integrates in-situ and satellite observations to characterize urban greenhouse gas (GHG) emissions across Houston, Texas USA. Surface-based background concentrations show seasonal reductions from similar to 435 to similar to 410 ppm for carbon dioxide (CO2) due to photosynthetic uptake, and from similar to 2.02 to similar to 1.88 ppm for methane (CH4) predominantly due to oxidation by the hydroxyl radical (OH). Boundary layer height corrected excess CO2 and CH4 (Delta CO2, Delta CH4) peak in winter (similar to 139.4 ppm, similar to 5.5 ppm) and drop in summer (similar to 5.6 ppm, similar to 0.08 ppm), highlighting emission seasonality. The observed annual Delta CH4/Delta CO2 ratio (9.4 ppb ppm(-1)) exceeds EDGAR and EPA inventory estimates by 65-70 %, and spatial mapping identifies key CH4 hotspots - such as McCarty and Blue Ridge landfills - with ratios larger than 40-70 ppb ppm(-1), which are severely underrepresented in emission inventories. Satellite-derived enhancements from OCO-3 and TROPOMI offer broader coverage but lack sensitivity to surface plumes. For example, in situ bivariate plots show sharp Delta CO2 enhancements >30 ppm and Delta CH4 up to similar to 0.3 ppm over industrial zones like the Ship Channel, while satellite Delta XCO2 (similar to 5-7 ppm) and Delta XCH4 (similar to 0.03-0.04 ppm) show moderate enhancements over the urban core. TROPOMI NO2 (similar to 1 x 10(-4) mu mol/m(2)) and HCHO (similar to 2.0 x 10(-4) mol/m(2)) enhancements further confirm co-located industrial emissions. This synthesis underscores the value of combining surface and satellite data for robust urban emission assessments and improved emission inventory evaluation.

During the period March 12-17, 2016, Mexico-City experienced its most severe smog episode since 2007. The Metropolitan Index of Air Quality (IMECA) for Mexico-City surpassed the value of 200, indicating an extremely bad situation. Hourly peak values for both, NO2 and O3, exceeded 200 ppb, while for CO more than 2 ppm were observed. Restrictions on traffic and industrial activities, among other emergency measures, were imposed. We describe results from Positive Matrix Factorization (PMF) for source apportionment based on a commixture of gasphase compounds (VOCs, CO, NO, NO2, SO2, NH3) along with equivalent black carbon (eBC), and ions (Na+, Mg2+, Ca2+, NO3-, NH4+) in combination with an analysis of regional meteorological processes and boundary layer conditions retrieved from continuous microwave radiometer measurements. Apart from more traditional emission sources, the PMF analysis also deciphered a geogenic source. Continuous boundary layer height data was used to normalize mixing ratios of pollutants representative for each source factor. This procedure allowed the retrieval of diurnal variations of pollutants predominantly determined by emissions and removal mechanisms. The results show prolonged daytime emissions of O3 precursors beyond the typical morning rush hour, an important information to optimize O3 mitigation strategies. Propylene Equivalent and Maximum Incremental Reactivity (MIR) methods identified isoprene and ethylene as the highest oxidant and O3 forming species which indicates some interchange of individual top VOC contributors to ozone formation in that city over the last decades. This presentation concludes with results from air quality modeling including Machine Learning approaches. While the Deep Neural Network, Random Forest and Gradient Tree Boosting models are depicting diurnal O3 levels nicely, as long as O3 mixing ratios are at moderate levels (≤120 ppb) only the Deep Neural Network may capture peak ozone values (>160 ppb), which are most critical with regard to public health.

A twenty-year (2004-2023) trend analysis of marine background air was conducted to explore potential changes in non-methane hydrocarbons (NMHCs) emissions and atmospheric oxidation capacity using the Propylene Equivalent (Propy-Equiv) concentration. The focus was on C2-C6 NMHCs including alkanes, aromatics, acetylene, and isoprene, as those were most frequently found in the air samples. During wintertime, least impacted by photochemical impacts, a clear increase in n-pentane was observed from 2004 to 2023 (2.07 f 2.26 % year- 1 ) (statistically significant). Ethane (-3.82 f 8.65 % year- 1 ) and n-butane (-1.35 f 15.62 % year- 1 ) decreased from 2004 to 2008, but this was not statistically significant, but a statistically significant increase was then observed until 2023 (ethane: 1.05 f 0.51 % year- 1 ; n-butane: 1.09 f 1.26 % year- 1 ). Iso-pentane decreased (-4.25 f 1.91 % year- 1 ) steadily from 2004 to 2011 (statistically significant), then remained constant but with increased variability until 2023 (0.28 f 2.49% year- 1 ). Propane increased (5.51 f 23 1.35% year- 1 ) from 2004 to 2014 (statistically significant) and decreased thereafter until 2023 (-3.63 f 3.91 % year- 1 ). Acetylene (-1.67 f 0.51 % year- 1 ), benzene (-2.43 f 0.14% year- 1 ), and i-butane (-0.58 f 250.25% year- 1 ) showed a steady decreasing (statistically significant) trend from 2004 to 2023. The increasing ethane trend for the last 15 years is due to global oil and natural gas extraction, especially in the US, which began in mid-2009. Improvements in gasoline technologies are causing the decline of acetylene and benzene trends. The slower than expected decreasing trend of acetylene mixing ratio might have been offset by the impact of biomass burning emissions. Other NMHCs show varying trends indicating the merge of different emission sources and strengths in separate time periods. During the summertime, 80-90% Propy-Equiv concentration is due to isoprene, with a statistically significant increasing trend (0.45 ppbC/year) between 2004 and 2023. This increase is largely due to rising temperatures (1.58 f 0.14 degrees C) leading to increased isoprene emissions (20 f 1.6%).

This study presents an analysis of hydroxyl (OH) and hydroperoxyl (HO2) radicals collectively referred to as HOx and their role in atmospheric chemistry within the Mexico City Metropolitan Area (MCMA). HOx radicals are paramount to the oxidative capacity of the atmosphere driving the formation of secondary pollutants such as ozone (O3) and secondary organic aerosols (SOA). A 0-D chemical box model (AtChem-2) was employed to simulate in situ production of OH and HO2 constrained by measurements of volatile organic compounds (VOCs), nitrogen oxides (NOx), O3, and other trace gases along with meteorological data collected during the campaign during the dry season. The analysis compares two distinct events: a pre-ozone episode with background conditions and an ozone episode characterized by strong photochemical activity confined to the MCMA. The results indicate significantly higher concentrations of OH and HO2 of 0.49 ppt and 25.95 ppt respectively during the ozone episode driven by enhanced photolysis of O3, HONO, and HCHO under clear sky conditions. HONO was identified as the dominant contributor with at least 2/3 to HOx production averaged over daytime, followed by O3 and HCHO. CO and VOCs have comparable effects on the loss rate of HOx while NOx plays the predominant role in the OH reactivity. The study reveals that elevated radical concentrations during the ozone episode corresponded to stronger and more persistent photochemical reactions by facilitating increased ozone formation of about 163 ppb. These findings underscore the importance of understanding radical production mechanisms in polluted urban environments particularly for the development strategies aimed at mitigating ozone and SOA levels.

Mexico City due to its specific topography and strong ozone precursors emissions often faces high surface ozone concentrations which negatively impact the dwellers and the environment of Mexico City. This necessitates developing models with the capacity to rank meteorological and air quality variables contributing to the build-up of ozone during an ozone episode in Mexico City. Such ranking is crucial for regulatory procedures aiming at reducing ozone detrimental effects during an ozone episode. In this study, three machine learning models (Random Forest, Gradient Boosting Tree, feedforward neural network) are used to learn a prediction function that reveals the functional dependence of ozone on its predictors and can predict hourly ozone concentrations using hourly data of eight predictors (nitric oxide, nitrogen dioxide, shortwave ultraviolet-A radiation, wind direction, wind speed, relative humidity, ambient surface temperature, planetary boundary layer height). The best model, feedforward neural network with 92% accuracy, in conjunction with Shapely Additive exPlanations approach, is utilized to simulate high ozone concentrations and rank the predictors according to their importance in the build-up of ozone during a severe ozone smog episode that occurred in the period 6 - 18 March 2016. The research focuses on Mexico City, but it is equally applicable to any other city in the world.

Lahore with an annual average of PM2.5 concentrations of 86.5 μg/m3 in 2021 was ranked among the top polluted cities of the world (https://www.iqair.com/us/world-air-quality-ranking). The COVID-19 pandemic altered the human mobility and economic activities immensely, as authorities enforced unprecedented lock down regulations. In order to reduce the spread of COVID-19, a complete lockdown was observed between 24 March – 31 May, 2020 in Pakistan. This paper aims at investigating the PM2.5, AOD and column amounts of six trace gases (NO2, SO2, CH4, HCHO, C2H2O2, and O3) by comparing periods of reduced emissions during lockdown periods with reference periods without emission reductions over Lahore, Pakistan. HYSPLIT cluster trajectory analyses were performed, which confirmed similar meteorological flow conditions during lockdown and reference periods. This provides confidence that any change in air quality conditions would be due to changes in human activities and associated emissions. The results show about 38% reduction in ambient surface PM2.5 levels during the lockdown period. This change also positively correlated with MODISDB and AERONETAOD data with a decrease of AOD by 42% and 35%, respectively. Reductions for tropospheric columns of NO2 and SO2 were about 20% and 50%, respectively during a semi lockdown period, while no reduction in the CH4, C2H2O2, HCHO and O3 levels occurred. During the lockdown period NO2, O3 and CH4 were about 40%, 45% and 25% lower, respectively, but no reduction in SO2, C2H2O2 and HCHO levels were noticed compared to the reference lockdown period for Lahore. HYSPLIT cluster trajectory analysis revealed the greatest impact on Lahore air quality through local emissions and regional transport from the east (agricultural burning and industry).

Volatile organic compounds (VOCs) data and other inorganic pollutants, in conjunction with surface meteorological data and planetary boundary layer height (PBLH) were collected at an urban site in Mexico City continuously from 6 to 18 March 2016. The zero-dimensional chemical box model AtChem2 was employed to investigate the in situ O3 generation in Mexico City. This model incorporates a subset of the Master Chemical Mechanism (MCM v3.3.1). AtChem2 was constrained to 29 VOCs, NO, NO2, SO2, HONO, temperature, pressure, relative humidity, PBLH, and photolysis rates (JNO2 as J4 and JO1 as J1). The Tropospheric Ultraviolet (TUV) radiation model, version 5.3, was utilized to calculate the radiation propagation through the atmosphere and determine the photolysis rates of NO2 and O1 in Mexico City. A series of modeling scenarios were conducted to explore how O3 generation responds to variations in VOCs and NOx. VOCs and NOx mixing ratios were adjusted independently, multiplying their mixing ratios by values ranging from 0.1 to 2.0. This variation of VOCs and NOx resulted in distinct model scenarios, allowing us to assess the sensitivity of O3 generation. It revealed that at least this area in Mexico City falls into a NOx limited area with regard to ozone formation. When only NOx is reduced, significant reduction in O3 is observed. While government interventions in Mexico City over the past decades have successfully reduced primary pollutant emissions, still episodes with high O3 levels occur, which require reassessment of O3 abatement strategies.

Mexico City frequently experiences high near-surface ozone concentrations, and exposure to elevated near-surface ozone causes harmful effects to the inhabitants and the environment of Mexico City. This necessitates developing models for Mexico City that predict near-surface ozone levels in advance. Such models are crucial for regulatory procedures and can save a great deal of near-surface ozone detrimental effects by serving as early warning systems. We utilize three machine-learning models, trained on seven-year data (2015–2021) and tested on one-year data (2022), to forecast the near-surface ozone concentrations. The trained models predict the next day’s 24-h near-surface ozone concentrations for up to one month; before forecasting the following months, the models are trained again and updated. Based on prediction results, the convolutional neural network outperforms the rest of the models on a yearly scale with an index of agreement of 0.93 for three stations, 0.92 for nine stations, and 0.91 for one station.

This study analyzes surface refractivity patterns and proposes six empirical models for its estimation in Houston, Texas, a subtropical climate region of the United States, using 18 years (2006-2023) of meteorological data. Monthly and yearly variations revealed a seasonal pattern peaking in summer (372.4 N units in July) and reaching its lowest in winter (327.8 N units in December). An analysis of the contributions of the dry and wet components shows the dry component dominating in the winter (61-63 %) and the wet component prevailing in the summer (56-60 %). The proposed models were developed using air temperature, atmospheric pressure, and relative humidity as input variables. Models' performances were evaluated using statistical metrics and the Akaike Information Criterion (AIC). The models' residuals closely followed a normal distribution, indicating robust and reliable predictive capabilities that enhance understanding of surface refractivity. Model 3, a twovariable linear model incorporating air temperature and atmospheric pressure (Ni = - 33.7689+ 1.3234(T) - 0.0069(P), was identified as the best performing model with the lowest AIC (6.829), AICC (12.543), and Delta AICC (0.000) values and deviations between -1.80 % and 2.38 % from the measured values. Model 1, a simpler single-variable linear model using only air temperature (Ni = - 41.2088+ 1.3250(T)), also showed strong performance, with deviations ranging from 0.12 % to 6.36 %. Comparative analysis indicated that locally developed models significantly outperformed the standard refractivity equation, highlighting the importance of location-specific empirical models for accurate surface refractivity estimation. Importantly, these models rely solely on readily accessible air temperature and atmospheric pressure measurements, eliminating the need for solar radiation data and enabling their easier application across diverse regions. These findings have implications for modeling radio wave propagation, radar systems, telecommunications planning, and atmospheric studies in similar climate regions.

Atmospheric turbidity exhibits substantial spatial–temporal variability due to factors such as aerosol emissions, seasonal changes, meteorology, and air mass transport. Investigating atmospheric turbidity is crucial for climatology, meteorology, and atmospheric pollution. This study investigates the variation in atmospheric turbidity over a tropical location in Nigeria, utilizing the Ångström exponent (α), the turbidity coefficient (β), the Linke turbidity factor (TL), the Ångström turbidity coefficient (βEST), the Unsworth–Monteith turbidity coefficient (KAUM), and the Schüepp turbidity coefficient (SCH). These parameters were estimated from a six-month uninterrupted aerosol optical depth dataset (January–June 2016) and a one-year dataset (January–December 2016) of solar radiation and meteorological data. An inverse correlation (R = −0.77) was obtained between α and β, which indicates different turbidity regimes based on particle size. TL and βEST exhibit pronounced seasonality, with higher turbidity during the dry season (TL = 9.62 and βEST = 0.60) compared to the rainy season (TL = 0.48 and βEST = 0.20) from May to October. Backward trajectories and wind patterns reveal that high-turbidity months align with north-easterly air flows from the Sahara Desert, transporting dust aerosols, while low-turbidity months coincide with humid maritime air masses originating from the Gulf of Guinea. Meteorological drivers like relative humidity and water vapor pressure are linked to turbidity levels, with an inverse exponential relationship observed between normalized turbidity coefficients and normalized water vapor pressure. This analysis provides insights into how air mass origin, wind patterns, and local climate factors impact atmospheric haze, particle characteristics, and solar attenuation variability in a tropical location across seasons. The findings can contribute to environmental studies and assist in modelling interactions between climate, weather, and atmospheric optical properties in the region.

The dominant fraction of anthropogenic volatile organic compound (VOC) emissions shifted from transportation fuels to volatile chemical products (VCP) in Los Angeles (LA) in 2010. This shift in VOC composition raises the question about the importance of VCP emissions for ozone (O3) formation. In this study, O3 chemistry during the CalNex 2010 was modeled using the Master Chemical Mechanism (MCM) version 3.3.1 and a detailed representation of VCP emissions based on measurements combined with inventory estimates. The model calculations indicate that VCP emissions contributed to 23% of the mean daily maximum 8-hr average O3 (DMA8 O3) during the O3 episodes. The simulated OH reactivity, including the contribution from VCP emissions, aligns with observations. Additionally, this framework was employed using four lumped mechanisms with simplified representations of emissions and chemistry. RACM2-VCP showed the closest agreement with MCM, with a slight 4% increase in average DMA8 O3 (65 +/- 13 ppb), whereas RACM2 (58 +/- 13 ppb) and SAPRC07B (59 +/- 14 ppb) exhibited slightly lower levels. CB6r2, however, recorded reduced concentrations (37 +/- 10 ppb). Although emissions of O3 precursors have declined in LA since 2010, O3 levels have not decreased significantly. Model results ascribed this trend to the rapid reduction in NOX emissions. Moreover, given the impact of COVID-19, an analysis of 2020 reveals a shift to a NOX-limited O3 formation regime in LA, thereby diminishing the influence of VCPs. This study provides new insights into the impact of VCP emissions on O3 pollution from an in-depth photochemical perspective. In the 2010 CalNex study, researchers found that volatile organic compounds (VOCs) were predominantly emitted from volatile chemical products (VCPs) like solvents. In our subsequent research, using a detailed chemical model, we discovered that about a quarter of the mean daily maximum 8-hr average ozone was contributed by these everyday products during ozone episodes. This insight is critical as it underscores how these emissions significantly speed up ozone formation through accelerated chemical reactions. We also evaluated various simplified chemical mechanisms for ozone prediction, finding that the one incorporating key reactions of VOC species was most accurate. Further, our analysis suggests that the slight increase in Los Angeles' ozone levels since 2010 can likely be attributed to faster reductions in NOX emissions relative to VOCs. Notably, the reduction in NOX emissions by 2020 considerably diminished the impact of VCPs on ozone levels. However, given the uncertain future of NOX emissions and the ongoing rise in emissions from everyday products, it remains essential to control these emissions. Volatile chemical product emissions accounted for 23% of mean daily max 8-hr ozone in ozone episodes, as per the 2010 CalNex study VCP emissions boost ozone formation by increasing primary production and efficient recycling of ROx radicals, thus enhancing pollution 2020s reduction in NOx emissions in Los Angeles, potentially due to COVID-19, led to a NOx-limited state, lessening VCPs' impact on ozone

Volatile organic compounds (VOCs) are major ingredients of photochemical smog. It is essential to know the spatial and temporal variation of VOC emissions. In this study, we used the Positive Matrix Factorization (PMF) model for VOC source apportionment in Mexico City. We first analyzed a data set collected during the ozone season from March–May 2016. It includes 33 VOCs, nitrogen oxide (NO), nitrogen dioxide (NO2), the sum of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2) and particle matter with a diameter < 1 μm (PM1). Another PMF analysis focused only on VOC data obtained in the month of May between the years 2016, 2017, 2018, 2021, and 2022 to gain insights into interannual variations. While the use of fossil fuel through combustion and evaporation continues to be major fraction in Mexico City, additional sources could be identified. Apart from biogenic sources which become more important closer to the end of the ozone season, a second natural emission factor termed “geogenic”, was identified. Overall, anthropogenic sources range between 80–90%. Diurnal plots and bivariate plots show the relative importance of these emission source factors on different temporal and spatial scales, which can be applied in emission control policies for Mexico City.

Abstract In this study we analyze an air quality data set, which was collected in the vicinity of the center of Mexico City during the ozone season from March to May 2016. It includes 33 volatile organic compounds (VOCs), nitrogen oxide (NO), nitrogen dioxide (NO2), the sum of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2) and particle matter with a diameter less than 1 μm (PM1). We use the Positive Matrix Factorization (PMF) receptor model to apportion ambient VOC concentrations into nine emission source factors, The analysis for all the data reveals the following breakdown of emission factors: secondary aerosol precursors, accounting for 21.7% of the total mass, followed by the source factors NO2 (20.4%), traffic-1 (19.3%), traffic-2 (16.2%), liquefied petroleum gas (LPG) (7.3%), geogenic (5.3%), biogenic (4.5%), incomplete fuel evaporation (2.7%), and solvents (2.2%). While the use of fossil fuel through various types of combustion and evaporation continues to be major fraction in Mexico City, additional sources could be identified. Apart from biogenic sources which tend to be more important closer to the end of the ozone season a second natural emission factors termed “geogenic” was identified. Overall, anthropogenic sources contribute almost 90%. Diurnal plots and bivariate plots show the relative importance of these emission source factors on different temporal and spatial scales which can be applied in emission control policies for Mexico City.

An analysis of the sea breeze, its mechanisms and correlation with precipitation was performed in two cases for an urban city on the Gulf Coast where the first case examined pure sea breezes and the second investigated those associated with a likelihood for appreciable inland precipitation. Adjacent study areas to the east and west were used as background conditions to determine if changes in Houston's sea breeze are controlled by urbanization or climate change. Sea breeze days were selected by a three-filter algorithm using the difference in land and sea surface temperatures, & UDelta;TSB, as well as geostrophic and surface winds. Results showed a decrease of 3-21% in sea breeze days between cases in the Houston area. Case 2 exhibited the most (e.g., 524 sea breeze days) and greatest increase (0.63 day yr- 1) in sea breeze days in Beaumont, driven by an increase in land surface temperature which is statistically significant (p-value = 0.02) and decrease in sea surface temperature leading to an increase in & UDelta;TSB, again with statistical significance (p-value = 0.01). Precipitation has increased by at least 1.07 x 10-8 mm day- 1, with the greatest increase of 2.59 x 10-8 mm day- 1 in Beaumont. The only increase in precipitation that was not found to be statistically significant existed for the most inland station in Houston. Based on an areal analysis of land cover, Houston is significantly more urban than the adjacent study areas: 65.8% of its area falls into the four developed classes, and these classes have increased by 11.4% between 2001 and 2019. Comparatively, Beaumont and Victoria are 16.8% and 7.16% developed, and urban development increased by 1.20% and 0.61%, respectively, over the same period. This indicates the possibility that the roughness elements in Houston's built environment interfere with the sea breeze, while climate change has driven its increase in Beaumont and decrease in Victoria as evidenced by our reported changes in meteorological variables.

Volatile organic compounds (VOCs) data in conjunction with other inorganic pollutants, surface meteorological data and continuous measurement of the Planetary Boundary Layer height (PBLH) at an urban site in Mexico City were performed from 6 to 18 March 2016. Positive Matrix Factorization (PMF) identified four emission source factors of VOCs along with equivalent black carbon (eBC), gaseous pollutants (CO, NO, NO2, SO2, NH3) and ions (Na+, Mg2+, Ca2+, NO3-, NH4+): (1) secondary aerosol precursors, (2) evaporation and non-LPG fuel combustion, (3) geogenic source and (4) vehicle exhaust. Propylene Equivalent and Maximum Incremental Reactivity (MIR) methods identified isoprene and ethylene as the highest oxidant and O-3 forming species. Pollutant data normalized to the variation of the PBLH revealed continued production of O-3 precursors in the afternoon beyond the typical morning rush hour. In particular this could be observed during the second part of the measurement period (12-15 March) when a strong O-3 episode occurred under weak wind and lower PBLH conditions compared to the preceding period (6-11 March) when well mixed conditions due to elevated daytime PBLH and strong advection led to overall reduced pollutant mixing ratios in the afternoon hours.

Nitrophenols (NPs) have significant impacts on human health, climate, and atmospheric chemistry. Despite numerous measurements of particulate NPs, still little is known about their gaseous atmospheric abundances, sources, and fate. Here, four gaseous NPs [2,4-dinitrophenol (2,4-DNP), 4-nitrophenol (4-NP), 2-nitrophenol (2-NP), and 2-Methyl-4-nitrophenol (2-Me-4-NP)] were continuously monitored during late Spring at an urban site in Houston, Texas. Among the four NPs, 4-NP showed the highest abundance, followed by 2-Me-4-NP, 2-NP, and 2,4-DNP with average concentrations of 1.07 ± 0.19 ppt, 0.47 ± 0.12 ppt, 0.41 ± 0.16 ppt, and 0.27 ± 0.09 ppt, respectively. The positive matrix factorization (PMF) model identified seven sources: industrial NPs, secondary formation, phenol sources, acetonitrile source, natural gas/crude oil, traffic, and petrochemical industries/oil refineries. A zero-dimensional photochemical box model was used to simulate the observed 2-NP and 2,4-DNP. A 50.0% and 70.0% jNO2 was found to be consistent with the measured 2-NP and 2,4-DNP. This yields a nitrous acid (HONO) production of 7.5 ± 2.5 ppt/h from 06:00 to 18:00 Central Standard Time (CST) from both NPs. An extrapolation including other known NPs suggests a maximum HONO formation of 13.8 ppt/h. The results of this study suggest that using PMF analysis supplemented by photochemical box model provides identification of the NPs sources and their atmospheric implication to HONO formation.

The COVID-19 pandemic altered the human mobility and economic activities immensely, as authorities enforced unprecedented lock down regulations. In order to reduce the spread of COVID-19, a complete lockdown was observed between 24 March – 31 May 2020 in Pakistan. This paper aims at investigating the PM2.5, AOD and column amounts of six trace gases (NO2, SO2, CH4, HCHO, C2H2O2, and O3) by comparing periods of reduced emissions during lockdown periods with reference periods without emission reductions over Lahore, Pakistan. HYSPLIT cluster trajectory analyses were performed, which confirmed similar meteorological flow conditions during lockdown and reference periods. This provides confidence that any change in air quality conditions would be due to changes in human activities and associated emissions. The results show about 38% reduction in ambient surface PM2.5 levels during the lockdown period. This change also positively correlated with MODISDB and AERONETAOD data with a decrease of AOD by 42% and 35%, respectively. Reductions for tropospheric columns of NO2 and SO2 were about 20% and 50%, respectively during a semi lockdown period, while no reduction in the CH4, C2H2O2, HCHO and O3 levels occurred. During the lockdown period NO2, O3 and CH4 were about 50%, 45% and 25% lower, respectively, but no reduction in SO2, C2H2O2 and HCHO levels were noticed compared to the reference lockdown period for Lahore. HYSPLIT cluster trajectory analysis revealed the greatest impact on Lahore air quality through local emissions and regional transport from the east (agricultural burning and industry).

Knowledge of solar radiation and its components in a particular area is crucial in studying solar energy and constructing solar energy devices due to the many advantages solar radiation has over fossil fuels. In this two-year study, conducted at a tropical site in Ile-Ife, Nigeria, from January 2016 to December 2017, twenty-one empirical models were proposed to estimate diffuse solar radiation using continuous solar radiation data. The models were divided into five groups and developed using relative sunshine duration and/or clearness index as input variables. The performance of five models from the literature was also examined and compared to measured data. The models' performance was evaluated using the Akaike Information Criteria (AIC), the Global Performance Index (GPI), and various statistical errors. Model 11, a quadratic model with clearness index as an input variable, had the lowest AIC (1.8098), AICC (4.8099), ∆AICC (0.0000), and GPI (-2.1796) values and was the most accurate model for estimating diffuse solar radiation at the study site and other locations with similar climatic conditions. None of the models selected from the literature was suitable for estimating diffuse solar radiation at the study site; hence, the proposed models performed better.