Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 µg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

Utilizing social media for community risk communication in megacities: analysing the impact of WeChat group information interaction and perception on communication satisfaction during the COVID-19 pandemic in Shanghai.

BACKGROUND: Against the backdrop of the global public health crisis, the COVID-19 pandemic has exposed significant disparities in the supply and demand of risk information related to public health crises, posing severe challenges to risk governance in megacities. Shanghai, China, introduced community WeChat groups for community communication, effectively facilitating the dissemination and response of grassroots information and providing a new path for interactive governance in the community. METHODS: This study collected 1006 questionnaires from residents of 350 communities in Shanghai through an online survey between June 10 and July 10, 2022. Multiple linear regression analysis was conducted to examine the impact of different participants (including the community, core residents, and the combined community and core residents) on community risk communication, perceived communication quality, and dissemination themes related to COVID-19 on community communication satisfaction. Additionally, in-depth interviews were conducted with 20 core residents from different types of communities, focusing on the specific methods of risk communication through community WeChat groups and their ability to disseminate information, respond to, and solve problems. RESULTS: Perceived information coverage and perceived response efficiency are significantly positively correlated with communication satisfaction. Notably, the speed of community information response has the greatest impact on communication satisfaction. Regarding COVID-19-related information dissemination themes, "community outbreaks, supplies, nucleic acids, outbreak prevention measures, and scientific content" all have a significant impact on communication effectiveness, with "nucleic acid testing information" having the greatest impact. Although the statistical data indicate that the participation of core residents in risk communication does not significantly affect communication satisfaction, it seems to be related to the size of the community, and the interview results further validate this conclusion. CONCLUSION: In the future, grassroots communities should consider the affordances of social media, recognize the significant correlation between risk communication and grassroots trust, and formulate more detailed and targeted risk communication strategies. In particular, incorporating core residents into "semiformal" grassroots organizations can improve community service quality, thereby enhancing community resilience in the face of public health emergencies.

Analysis on the impact of smart city construction on urban greenness in China's megacities.

Urban greenness serves as a key indicator of sustainable urban development, with smart city construction emerging as a primary strategy for its enhancement. However, there is little empirical evidence considering multi-dimension between urban greenness and smart city construction on the city level. This study focuses on the impact on urban greenness of smart city construction in megacities, using the difference-in-differences regression model to evaluate the impact based on urban development conditions in various aspects from 2010 to 2021 in 10 megacities in China. The results of panel data of different indicator samples show unique conclusions. First, smart city pilot policy in megacities has significant impact on urban greenness, primarily due to demographic and economic developments. Second, the impact is different between the megacity and national level, and different factors of urban greenness have different effects on smart city construction. Third, the effects are time-lagged and lasted for years, and regional heterogeneity divided by building climate zones is existed, where the effect is more obvious in city agglomeration. These findings of smart city construction reveal the unique influences on megacity greenness, and can be generalized to cities with similar characteristics accordingly.

Wet Deposition of Globally Transportable Microplastics (<25 µm) Hovering over the Megacity of Beijing.

Microplastics of size <25 µm possess globally transportable features, but the impact of precipitation on their transport remains unclear. Here, microplastics were detected in all 10 studied rainfalls in Beijing, with <25 µm microplastics present in 8 rainfalls. Interestingly, microplastic abundance (7590-136,778 items·m-3) was tentatively linked to maximum rainfall intensity, with <25 µm microplastics making up 39.6 (±27.5)% of the total count. The composition of <25 µm microplastics differed from that of larger microplastics, although both mainly comprised polystyrene, polyethylene, and polypropylene. The microplastic communities differed among rainfalls, suggesting that atmospheric transport is a highly dynamic process. The first rainfall exhibited the highest microplastic abundance and community diversity after long-term exposure to dry atmospheric environment. The deposited microplastics were unstable and highly fragmented according to the conditional fragmentation model. The wet deposition rate of the microplastics was calculated as 2-463 µg·m-2 (146-8629 items·m-2) per rain, amounting to 25.44 tons per annum in Beijing. Although <25 µm microplastics represented a negligible proportion (0.00-1.24%) of the overall mass load of microplastics, their numerical abundance was high. Our results demonstrate that precipitation is an effective mechanism for removing airborne microplastics, which may enter urban soils and waters, exacerbate microplastic burdens in the environment, and cause potential risk for human health.

Cross-sectional evaluation of the association between greenness and cognitive performance in Mexican pre-pubertal boys.

BACKGROUND: Evidence shows that greenspace exposure benefits children's health and cognitive development. However, evidence assessing this association in young children in low- and middle-income economies is scarce. OBJECTIVE: To assess the association between exposure to greenness and cognitive performance in pre-pubertal boys living in Mexico City. METHODS: Cross-sectional study using data from 144 boys aged 6-11 years living in Mexico City in 2017 and enrolled in the "MetCog" study. Cognitive performance was evaluated through selected Wechsler Scale for Intelligence in Children Fourth Edition (WISC-IV) and Neuropsychological Assessment of Children (Evaluación Neuropsicológica Infantil, ENI) tests. Exposure to greenness was assessed through Normalised Difference Vegetation Index (NDVI) at 300, 500, 1500, 2000, and 3000 m buffer zones from children's residences. Multiple linear regression analysis was undertaken to assess associations between cognitive performance and greenness (aß) with 95% confidence intervals (CI) and adjusted for potential confounding variables. Significance was set at q < 0.05 after False Discovery Rate (FDR) correction. RESULTS: A positive association was found between the NDVI Interquartile Range (IQR) at 2000 m and the WISC-IV block design test score (aß 2000 = 1.18, 95% CI = 0.31, 2.06; q < 0.05), which assesses perceptual reasoning. Positive associations were found with NDVI IQR at 1500 m and WISC-IV block design (aß1500 = 1.00, 95% CI = 0.14, 1.86) and matrix reasoning (aß1500 = 0.83, 95% CI = 0.06, 1.61) scores, but neither survived FDR correction. No significant associations were found between NDVI IQR at any buffer size with other WISC-IV and ENI task scores. CONCLUSIONS: Greater exposure to greenness was associated with higher perceptual reasoning skills in 144 pre-pubertal boys living in Mexico City. Thus, urban planning should consider increasing vegetation in megacities, especially in neighbourhoods with high percentages of young children.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Enabling country-scale land cover mapping with meter-resolution satellite imagery.

High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts of satellite imageries caused by, e.g., geographical differences or acquisition conditions, few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. To fill this gap, we present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. In addition, we propose a deep-learning-based unsupervised domain adaptation approach that can transfer classification models trained on labeled dataset (referred to as the source domain) to unlabeled data (referred to as the target domain) for large-scale land cover mapping. Specifically, we introduce an end-to-end Siamese network employing dynamic pseudo-label assignment and class balancing strategy to perform adaptive domain joint learning. To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images. Over a total study area of 60,000 km2, the experiments show promising results even though the input images are entirely unlabeled. The proposed approach, trained with the Five-Billion-Pixels dataset, enables high-quality and detailed land cover mapping across the whole country of China and some other Asian countries at meter-resolution.

High-carbon expansion or low-carbon intensive and mixed land-use? Recent observations from megacities in developing countries: A case study of Shanghai, China.

Cities have become significant sources of greenhouse gas emissions. Effective land management may be the solution to carbon neutrality targets for megacities with limited land resources. This paper takes Shanghai as a case study to investigate the regional land use dynamics and its impact on carbon emissions following the implementation of land conservation and intensive use policy. During 2010-2020, the land use pattern in Shanghai changed from the previous urban land expansion to a combination of industrial land reduction and woodland expansion. Meanwhile, the area proportion of land-use mixture grids increased from 90.50% to 92.28% with the spatial pattern of mixed types also changing. Furthermore, the notable land-use mixture does not necessarily lead to carbon emission reduction, but it can reduce carbon emission hotspots in industrial agglomerations by promoting the mixed use of industrial land and other land use types. However, megacities cannot achieve carbon balance through land use management alone. Due to the increasing carbon emission density of hybrid industrial land, the joint implementation of a land conservation and intensive use strategy with industrial and energy structure adjustments may be an effective way forward.

The relationship between PM10 and meteorological variables in the mega city Istanbul.

PM10, one of the air pollutants, occurs regularly in Istanbul during the winter months, namely in December, January, and February. PM10 pollutant is affected by numerous factors. Among these factors are various meteorological variables and climatological factors. This article aims to determine the relationship between PM10 and meteorological variables (wind speed, wind direction, temperature, and relative humidity) and to interpret these results. PM10 and meteorological data were examined between 2011 and 2018. To determine the relationship, multiple linear regression, Pearson's correlation coefficient (PCC), Spearman's rank correlation, Kendall Tau correlation, autocorrelation function (ACF), cross-correlation function (CCF), and visuals were determined using the R program (open-air) packages. In the study, the relationship between wind, temperature, and relative humidity with PM10 was determined, and it was observed that the PM10 concentration was maximum between January and February. PM10 concentrations have a positive relationship with relative humidity and wind direction, while a negative relationship with wind speed and temperature was observed. The correlation values for relative humidity and temperature were found to be 0.01 and - 0.15, respectively. Furthermore, the relationship between wind speed and PM10 was calculated from multiple linear regression model, and the estimated value was - 0.12 while looking at the wind direction value, it was approximately 0.03.

Will different land uses affect heavy metal pollution in soils of roadside trees? An empirical study from Shanghai.

Heavy metal pollution in roadside soil may harm humans, animals, plants, and local ecosystems. This study aimed to explore the sources and potential ecological risks of heavy metals in soils of roadside trees under different land uses, using soil samples collected from 136 roads across 16 administrative districts in Shanghai. The contents, pollution characteristics, potential ecological risks, and sources of seven heavy metals were analyzed, including Cr, Ni, Cd, Pb, As, Cu, and Zn. Results showed that (1) land use patterns affected the heavy metal contents, with industrial and construction areas showing higher contents while agricultural and forestry areas lower; (2) the ranking of heavy metal pollution levels was Cd > As > Pb > Cu > Ni > Cr > Zn. Cd exhibited the highest potential ecological risk, falling within the moderate to considerable potential ecological risk interval; (3) the sources of Cu, Zn, Cr, Ni, Cd, and Pb were associated with traffic emissions, whereas As had independent other sources and Pb in industrial and construction areas was also influenced by industrial emissions. These results provide valuable references on the control of heavy metal pollutants and the management of land uses in megacities.

Urbanisation threats to dairy cattle health: Insights from Greater Bengaluru, India.

Complex urbanisation dynamics, on the one hand, create a high demand for animal products, and on the other hand put enormous pressure on arable land with negative consequences for animal feed production. To explore the impact of accelerated urbanisation on dairy cattle health in urban farming systems, 151 farmers from different parts of the Greater Bengaluru metropolitan area in India were individually interviewed on aspects addressing cattle management and cattle health. In addition, 97 samples of forages from the shores of 10 different lakes, and vegetable leftovers used in cattle feeding were collected for nutritional analysis. Along with the use of cultivated forages, crop residues, and concentrate feed, 47% and 77% of the farmers occasionally or frequently used lake fodder and food leftovers, respectively. Nutritionally, lake fodder corresponded to high-quality pasture vegetation, but 43% of the samples contained toxic heavy metals such as arsenic, cadmium, chromium, and lead above official critical threshold levels. Therefore, lake fodder may affect cows' health if consumed regularly; however, heavy metal concentrations varied between lakes (P < 0.05), but not between fodder types (P > 0.05). Although 60% of the interviewed farmers believed that their cows were in good health, logit model applications revealed that insufficient drinking water supply and the use of lake fodder negatively impacted cattle health (P < 0.05). While it remains unknown if regular feeding of lake fodder results in heavy metal accumulation in animal products, farmers and farm advisors must address this and other urbanization-related challenges to protect cattle health.

Effect of Biomass Burning, Diwali Fireworks, and Polluted Fog Events on the Oxidative Potential of Fine Ambient Particulate Matter in Delhi, India.

We investigated the influence of biomass burning (BURN), Diwali fireworks, and fog events on the ambient fine particulate matter (PM2.5) oxidative potential (OP) during the postmonsoon (PMON) and winter season in Delhi, India. The real-time hourly averaged OP (based on a dithiothreitol assay) and PM2.5 chemical composition were measured intermittently from October 2019 to January 2020. The peak extrinsic OP (OPv: normalized by the volume of air) was observed during the winter fog (WFOG) (5.23 ± 4.6 nmol·min-1·m-3), whereas the intrinsic OP (OPm; normalized by the PM2.5 mass) was the highest during the Diwali firework-influenced period (29.4 ± 18.48 pmol·min-1·µg-1). Source apportionment analysis using positive matrix factorization revealed that traffic + resuspended dust-related emissions (39%) and secondary sulfate + oxidized organic aerosols (38%) were driving the OPv during the PMON period, whereas BURN aerosols dominated (37%) the OPv during the WFOG period. Firework-related emissions became a significant contributor (∼32%) to the OPv during the Diwali period (4 day period from October 26 to 29), and its contribution peaked (72%) on the night of Diwali. Discerning the influence of seasonal and episodic sources on health-relevant properties of PM2.5, such as OP, could help better understand the causal relationships between PM2.5 and health effects in India.

The carbon emissions related to the land-use changes from 2000 to 2015 in Shenzhen, China: Implication for exploring low-carbon development in megacities.

Megacities exploit enormous amounts of lands from outside of the city boundary. However, there is a large knowledge gap in the impact of socioeconomic activities associated land-use changes on carbon emissions of megacities during the urbanization. In the current work, we combined the material-flow analysis, environmental extended input-output model, and land matrix data to construct a hybrid network framework. Such a framework was used to estimate the carbon emissions driving from trade between sectors and associated land use changes during 2000-2015 in Shenzhen, China. Results indicated that the total carbon emissions of Shenzhen had a growth rate of 262.7% from 2000 to 2010 and a declining rate of 17.6% from 2010 to 2015. This pattern is associated with large declining rates in the overall energy and carbon intensities by 53.8% and 63.2% during the period of 2000-2015. Meanwhile, embodied carbon emissions of Shenzhen kept rising by approximately twofold, accompanied by the increasing trends in the land-use related carbon emissions both inside and outside of city boundary. The land uses per unit GDP showed a dramatical decline by 85.7% and with a large contribution of the transportation and industrial land, and this caused a gradual increase in overall land-use related emissions with average growth rate of 7.1%. In addition, the land-use change related carbon emissions of the transportation and industrial land had a cumulative growth of 85%. As for the embodied land-use related carbon emissions, the dominated contributor was the Agriculture sector which drove an average of 0.13 MtC yr-1 emissions via importing agricultural products from outside of Shenzhen. This study provides a scientific foundation for corporately mitigate carbon emissions between megacities and their surrounding regions.

Characterization of Regional Combustion Efficiency using ΔXCO: ΔXCO2 Observed by a Portable Fourier-Transform Spectrometer at an Urban Site in Beijing.

Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide, CO2 (XCO2) and CO (XCO), were performed throughout 2019 at an urban site in Beijing using a compact Fourier Transform Spectrometer (FTS) EM27/SUN. This data set is used to assess the characteristics of combustion-related CO2 emissions of urban Beijing by analyzing the correlated daily anomalies of XCO and XCO2 (e.g., ΔXCO and ΔXCO2). The EM27/SUN measurements were calibrated to a 125HR-FTS at the Xianghe station by an extra EM27/SUN instrument transferred between two sites. The ratio of ΔXCO over ΔXCO2 (ΔXCO:ΔXCO2) is used to estimate the combustion efficiency in the Beijing region. A high correlation coefficient (0.86) between ΔXCO and ΔXCO2 is observed. The CO:CO2 emission ratio estimated from inventories is higher than the observed ΔXCO:ΔXCO2 (10.46 ± 0.11 ppb ppm-1) by 42.54%-101.15%, indicating an underestimation in combustion efficiency in the inventories. Daily ΔXCO:ΔXCO2 are influenced by transportation governed by weather conditions, except for days in summer when the correlation is low due to the terrestrial biotic activity. By convolving the column footprint [ppm (µmol m-2 s-1)-1] generated by the Weather Research and Forecasting-X-Stochastic Time-Inverted Lagrangian Transport models (WRF-X-STILT) with two fossil-fuel emission inventories (the Multi-resolution Emission Inventory for China (MEIC) and the Peking University (PKU) inventory), the observed enhancements of CO2 and CO were used to evaluate the regional emissions. The CO2 emissions appear to be underestimated by 11% and 49% for the MEIC and PKU inventories, respectively, while CO emissions were overestimated by MEIC (30%) and PKU (35%) in the Beijing area.

Occurrence of priority substances in urban wastewaters of Istanbul and the estimation of the associated risks in the effluents.

Increase in the contamination of the aquatic environments is a global challenge; hence, understanding the sources of priority substances (PSs) is essential. In an attempt to implement this principle, a year-long monitoring covering all seasons was carried out in the influents and effluents of four largest wastewater treatment plants (WWTPs) in Istanbul. Results obtained showed the presence of 48 PSs (66% of the target compounds) including pesticides, polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), dioxins and dioxin-like compounds (DLCs), alkylphenols, phthalates, and metals ranging from low nanograms to micrograms per liter. Priority hazardous substances that were banned for long were still found to be present in wastewaters. PAHs, DLCs, alkylphenols, and metals were found to be present in all samples. Di(2-ethylhexyl) phthalate (DEHP) and DLCs were detected in more than 80% of the influent samples. Trichloromethane had the highest concentrations among the most frequently (80-100%) detected PSs in the influents and effluents. The potential risks that may arise from WWTP effluents containing PSs were estimated by calculating the risk quotients (RQs). Upon the risk estimation conducted on the PSs in effluents, monitoring of the endrin, alpha-cypermethrin, theta-cypermethrin, zeta-cypermethrin, quinoxyfen, bifenox, benzo-ghi-perylene, and DEHP is recommended for the WWTP effluents.

Chemistry of Atmospheric Fine Particles During the COVID-19 Pandemic in a Megacity of Eastern China.

Air pollution in megacities represents one of the greatest environmental challenges. Our observed results show that the dramatic NOx decrease (77%) led to significant O3 increases (a factor of 2) during the COVID-19 lockdown in megacity Hangzhou, China. Model simulations further demonstrate large increases of daytime OH and HO2 radicals and nighttime NO3 radical, which can promote the gas-phase reaction and nocturnal multiphase chemistry. Therefore, enhanced NO3 - and SO4 2- formation was observed during the COVID-19 lockdown because of the enhanced oxidizing capacity. The PM2.5 decrease was only partially offset by enhanced aerosol formation with its reduction reaching 50%. In particular, NO3 - decreased largely by 68%. PM2.5 chemical analysis reveals that vehicular emissions mainly contributed to PM2.5 under normal conditions in Hangzhou. Whereas, stationary sources dominated the residual PM2.5 during the COVID-19 lockdown. This study provides evidence that large reductions in vehicular emissions can effectively mitigate air pollution in megacities.

Effect of COVID-19 pandemic-induced lockdown (general holiday) on air quality of Dhaka City.

A worldwide pandemic of COVID-19 has forced the Government of Bangladesh to implement a lockdown during April-May 2020 by restricting people's movement; shutdown of industries and motor vehicles; and closing markets, public places, and schools to contain the virus. This type of strict measures caused an outcome, the reduction of urban air pollution, around the world. The present study aims to investigate the reduction of the concentration of pollutants in the air of Dhaka City and the reduction of the Air Quality Index (AQI). Necessary time-series data of the concentration of PM2.5, NO2, SO2, and CO have been collected from the archive of the Air Quality Monitoring Station of the US Embassy in Dhaka and Sentinel-5P. The time-series data have been analyzed by descriptive statistics, and AQI was calculated following an appropriate formula suggested by the Environmental Protection Agency (EPA) based on the criteria pollutants. The study found that the concentrations of PM2.5, NO2, SO2, and CO during April-May 2020 have been reduced by 26, 30, 07, and 07%, respectively, compared with the preceding year's concentrations. Moreover, the AQI has also been reduced by about 35% on average during the lockdown period than the same times of the previous year. However, the magnitude of pollution reduction in Dhaka is lower than in other cities and countries globally, including Delhi, Sao Paulo, Wuhan, Spain, Italy, the USA. The main reasons may include, among others, the poor implementation of lockdown (especially in the first week of April and the second fortnight of May), pre-existing pollution, transboundary pollution, incineration of solid waste, etc. This study will help policymakers figure out how to regulate pollution sources and improve the air quality of Dhaka.

Reaching health facilities in situations of emergency: qualitative study capturing experiences of pregnant women in Africa's largest megacity.

BACKGROUND: The consequences of delays in travel of pregnant women to reach facilities in emergency situations are well documented in literature. However, their decision-making and actual experiences of travel to health facilities when requiring emergency obstetric care (EmOC) remains a 'black box' of many unknowns to the health system, more so in megacities of low- and middle-income countries which are fraught with wide inequalities. METHODS: This in-depth study on travel of pregnant women in Africa's largest megacity, Lagos, is based on interviews conducted between September 2019 and January 2020 with 47 women and 11 of their relatives who presented at comprehensive EmOC facilities in situations of emergency, requiring some EmOC services. Following familiarisation, coding, and searching for patterns, the data was analysed for emerging themes. RESULTS: Despite recognising danger signs, pregnant women are often faced with conundrums on "when", "where" and "how" to reach EmOC facilities. While the decision-making process is a shared activity amongst all women, the available choices vary depending on socio-economic status. Women preferred to travel to facilities deemed to have "nicer" health workers, even if these were farther from home. Reported travel time was between 5 and 240 min in daytime and 5-40 min at night. Many women reported facing remarkably similar travel experiences, with varied challenges faced in the daytime (traffic congestion) compared to night-time (security concerns and scarcity of public transportation). This was irrespective of their age, socio-economic background, or obstetric history. However, the extent to which this experience impacted on their ability to reach facilities depended on their agency and support systems. Travel experience was better if they had a personal vehicle for travel at night, support of relatives or direct/indirect connections with senior health workers at comprehensive EmOC facilities. Referral barriers between facilities further prolonged delays and increased cost of travel for many women. CONCLUSION: If the goal, to leave no one behind, remains a priority, in addition to other health systems strengthening interventions, referral systems need to be improved. Advocacy on policies to encourage women to utilise nearby functional facilities when in situations of emergency and private sector partnerships should be explored.

Managed retreat for climate change adaptation in coastal megacities: A comparison of policy and practice in Manila and Vancouver.

'Managed retreat' is gaining considerable attention as part of megacities' climate change adaptation and resilience enhancement toolkits, and as a policy option for disaster risk reduction in coastal regions. The overarching objective of managed retreat is to reduce the exposure of people and assets to flooding, storm surges and sea level rise by retreating from these threats in a planned fashion. Managed retreat is one of four main options covered in the 'PARA' (protect/accommodate/retreat/avoid) framework explained in this paper, which can be used to enhance resilience in coastal megacities. In this paper, qualitative research methods were used to collect primary data on the feasibility of using managed retreat for two case study coastal megacities: Manila, Philippines, and Vancouver, Canada. Both case studies review the risk context of each city, local climate change adaptation/disaster risk reduction (CCA/DRR) policies linked to managed retreat, examples of managed retreat practice, and barriers to managed retreat identified through primary or secondary data analysis. Comparisons between the two cases are then carried out, and similarities and differences are highlighted. The paper concludes by suggesting possible means by which barriers to managed retreat might be overcome.

Occurrence, sources and ecological and human health risks of polycyclic aromatic hydrocarbons in soils from some functional areas of the Nigerian megacity, Lagos.

The study investigated the levels of the USEPA 16 PAHs in soils collected from selected functional areas (cemetery, commercial, industrial and residential areas) of the Nigerian megacity, Lagos. The soil samples were subjected to ultrasonic-assisted extraction in a 1:1 (v/v) mixture of dichloromethane/hexane, and the PAHs in the resulting extracts were determined by gas chromatography-mass spectrometry. The Σ16 PAHs in soils of these functional areas varied between 890-4675, 485-4513, 111-15,577 and 509-2047 µg kg-1 for cemetery, industrial, commercial and residential areas, respectively. The benzo(a)pyrene carcinogenic (BaPTEQ) and mutagenic equivalency (BaPMEQ) values of PAHs in these soils spanned from 523 to 1046 and 446 to 1129 µg kg-1, respectively. The hazard index values suggested that there are adverse (non-carcinogenic) health effects for a child's exposure to PAHs in soils of commercial areas. The cancer risk values resulting from an adult's and a child's exposure to PAHs in these urban soils via dermal contact and oral ingestion surpassed the target value of 10-6 which suggested that there is a considerable cancer risk relating to human exposure to PAHs in these urban soils. An ecological risk assessment making use of soil quality guidelines and risk quotients suggested a low ecological risk to organisms in soils of these functional areas except for those from commercial areas. PAH isomeric ratios and principal component analysis indicated that PAHs in these soils arise from petrogenic inputs, such as occasional spills of liquid petroleum fuels and discharges from automobile workshops and generator houses, as well as pyrogenic processes including traffic emissions and combustion of fossil fuels and biomass.