National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations.

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO2 models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO2. Our results suggest that street view imagery alone may provide sufficient information to explain NO2 variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R2 of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO2 pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

Real-time measurement and source apportionment of elements in Delhi's atmosphere.

Delhi, the capital of India, suffers from heavy local emissions as well as regional transport of air pollutants, resulting in severe aerosol loadings. To determine the sources of these pollutants, we have quantified the mass concentrations of 26 elements in airborne particles, measured by an online X-ray fluorescence spectrometer with time resolution between 30 min and 1 h. Measurements of PM10 and PM2.5 (particulate matter <10 µm and < 2.5 µm) were conducted during two consecutive winters (2018 and 2019) in Delhi. On average, 26 elements from Al to Pb made up ~25% and ~19% of the total PM10 mass (271 µg m-3 and 300 µg m-3) in 2018 and 2019, respectively. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, non-exhaust, an S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). All factors were resolved in both size ranges (but varying relative concentrations), comprising the following contributions to the elemental PM10 mass (in % average for 2018 and 2019): Cl-Br-Se (41.5%, 36.9%), dust (27.6%, 28.7%), non-exhaust (16.2%, 13.7%), S-rich (6.9%, 9.2%), SFC1 + SFC2 (4%, 7%), Pb-Sn-Se (2.3%, 1.66%), Cu-Cd-Pb (0.67%, 2.2%) and Cr-Ni-Mn (0.57%, 0.47%). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the Pb-Sn-Se, Cl-Br-Se and SFC2 factors prevailed for northwest winds (Pakistan, Punjab, Haryana and Delhi), while the Cu-Cd-Pb and S-rich factors originated from east (Nepal and Uttar Pradesh) and the Cr-Ni-Mn factor from northeast (Uttar Pradesh). In contrast, SFC1, dust and non-exhaust were not associated with any specific wind direction.

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region.

In the present study, we assessed for the first time the performance of our custom-designed low-cost Particulate Matter (PM) monitoring devices (Atmos) in measuring PM10 concentrations. We examined the ambient PM10 levels during an intense measurement campaign at two sites in the Delhi National Capital Region (NCR), India. In this study, we validated the un-calibrated Atmos for measuring ambient PM10 concentrations at highly polluted monitoring sites. PM10 concentration from Atmos, containing laser scattering-based Plantower PM sensor, was comparable with that measured from research-grade scanning mobility particle sizers (SMPS) in combination with optical particle sizers (OPS) and aerodynamic particle sizers (APS). The un-calibrated sensors often provided accurate PM10 measurements, particularly in capturing real-time hourly concentrations variations. Quantile-Quantile plots (QQ-plots) for data collected during the selected deployment period showed positively skewed PM10 datasets. Strong Spearman's rank-order correlations (rs = 0.64-0.83) between the studied instruments indicated the utility of low-cost Plantower PM sensors in measuring PM10 in the real-world context. Additionally, the heat map for weekly datasets demonstrated high R2 values, establishing the efficacy of PM sensor in PM10 measurement in highly polluted environmental conditions.

Amaurosis, an Unusual Complication Secondary to Inferior Alveolar Nerve Anesthesia: A Case Report and Literature Review.

Ocular complications after an inferior alveolar nerve anesthesia are rare. These complications, although temporary and benign, can be distressing to both the patient and the clinician. A 37-year-old male patient was administered an inferior alveolar nerve block for the root canal treatment of tooth #30. Immediately after the administration of local anesthesia, the patient reported complete loss of vision. The patient recovered completely after 15 minutes. In particular, amaurosis is quite uncommon and usually heralds a more sinister pathology such as stroke. This case report presents an unusual case of ocular complication after an inferior alveolar nerve block. Adequate knowledge of the regional anatomy and physiology of the orbit and its nearby structures, the proposed causes of ocular complications, and prevention and management is necessary to manage such events.

Comparative evaluation of microleakage of mineral trioxide aggregate and Geristore root-end filling materials in different environments: An in vitro study.

AIM: The aim of this in vitro study was to evaluate and compare the microleakage of mineral trioxide aggregate (MTA) and Geristore root-end filling materials in different environments. MATERIALS AND METHODS: After removing the anatomical crowns of ninety extracted human maxillary central incisors, their root canals were instrumented and obturated. The apical 3 mm of each root was resected, and a standardized root-end cavity was prepared using an ultrasonic tip. The roots were alienated into three equal subgroups for each material and the root-end filling was performed in different environments namely dry, saliva contaminated, and blood contaminated. Samples were immersed in 0.2% Rhodamine B dye for 48 h. Roots were sectioned longitudinally and examined under a fluorescent microscope to measure the linear dye penetration. The results were statistically analyzed using analysis of variance and Tukey's honestly significant difference post hoc test. RESULTS: The Geristore dry group illustrated the lowest linear leakage, while the MTA saliva-contaminated group illustrated the highest leakage. In dry environment, linear dye penetration of both MTA and Geristore groups did not show any significant difference. However, statistically significant difference was observed between MTA and Geristore groups in blood- and saliva-contaminated environments. CONCLUSION: Geristore showed better results in saliva- and blood-contaminated environments; hence, Geristore may be used as an alternative to MTA for root-end filling.