Ambient Sources Research Articles

Association between air pollutants, sources, and components of PM2.5 and pediatric outpatient visits for respiratory diseases in Shanghai, China

BackgroundAlthough exposure to ambient air pollution has been associated with outpatient visits for respiratory diseases, there have been few recent studies investigating the effect of the chemical components of ambient particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) on pediatric outpatient visits for respiratory diseases. In addition, there remains scarce evidence regarding PM2.5 components and sources that are most relevant to pediatric outpatient visits for respiratory diseases. This study aimed to determine the association between four ambient pollutants (SO2, NO2, PM10, and PM2.5), sources and chemical components of PM2.5, and pediatric outpatient visits for respiratory diseases. MethodsThis time-series study was conducted between January 1, 2018 and December 31, 2019 in Shanghai, China. A positive matrix factorization model was used to determine the source apportionment of PM2.5, and a generalized additive model was employed to evaluate the effect of air pollutants and the components and sources of PM2.5 on pediatric outpatient visits for respiratory diseases. In addition, a two-pollutant model was used to investigate the potential confounding effects of the copollutants. ResultsThe study revealed that a 10 μg/m3 increase in SO2, NO2, PM10, and PM2.5 concentrations corresponded to increases of 3.4% (95% CI: 2.3%–4.6%), 1.9% (95% CI: 1.7%–2.2%), 0.5% (95% CI: 0.4%–0.7%), and 0.5% (95% CI: 0.4%–0.7%), respectively, in pediatric outpatient visits for respiratory diseases on the day with the most significant lag effect. Notably, Ni exposure more strongly correlated with increased pediatric outpatient visits for respiratory diseases, with the percent of increase by 10 ng/m3 ranging from 7.6% to 16.5%. Moreover, among the PM2.5 sources researched in this study, oil and coal combustion were the most important for increasing pediatric outpatient visits for respiratory diseases. ConclusionThis study demonstrates that ambient pollutants and the chemical components and sources of PM2.5 were significantly associated with an elevated risk of pediatric outpatient visits for respiratory diseases and provides insights into the differential health risks of PM2.5 components.

LMJSSP for analysis of prophylactic lubricants, spermicides and residues

DNA evidence in sexual assault cases have proven increasingly difficult to obtain and analyse due to increased condom use. With more interest in alternatives to DNA evidence, prophylactic lubricants, spermicides and residues may be interesting prospects. Current interest in the analysis of prophylactic residues focuses on the evaluation and identification of lubricants and constituents, primarily through gas chromatography or Fourier transform infrared spectroscopy. Though cost-effective methods, extensive sample preparation and destructive modes of analysis remain an area for improvement. As a result, the focus has since shifted to ambient ionization methods that offer adequate sensitivity and reduced sample preparation. The Liquid Microjunction Surface Sampling Probe (LMJSSP) is a versatile ambient ionization source that employs a probe that supports a continuously flushing droplet that extracts analytes when placed in contact with a surface. The analytes are aspirated into the mass spectrometer with a Venturi pressure. In this work we use the LMJSSP to analyse the trace transfer of condom lubricant to different types of fabric (cotton, cotton-spandex, and denim). Furthermore, we examine the sensitivity and storage conditions for the direct analysis method on different swab types (cotton, silicone, and foam). Additionally, Principal Component Analysis (PCA) and Maximally Collapsing Metric Learning (MCML) are utilized for visualization of differentiability of commercially available condom brands including Durex™ and Trojan™, and product subtypes. The results present an interesting multi-disciplinary approach of using a direct liquid extraction ambient ionization technique and machine learning to improve the overall workflow for the analysis of lubricants, swabs and fabrics. Machine learning algorithms were able to differentiate between inherent differences of Durex™ and Trojan™ condoms.

Applying the 12 Principles of Green Engineering in Low TRL Electronics: A Case Study of an Energy-Harvesting Platform

Energy harvesting has been identified as a key enabling technology for the Internet of Things as it allows a battery-less functioning of electronic devices. While the use of ambient sources of energy is commonly seen as sustainable due to their renewable nature, raw material consumption and recyclability need to be assessed to ensure true sustainability. This is especially relevant in electronics, due to their high complexity stemming from the variety of components and materials in their composition. This work presents the case study of the application of the 12 Principles of Green Engineering to an energy-harvesting platform in the early technology development phase. Specifically, the technological areas of design for disassembly, materials for substitution, fabrication efficiency, and manufacturing processes that enable the use of recycled materials have been evaluated. This has allowed us to identify hazardous raw materials and recommend their substitution. Further recommendations include the adoption of mechanical fixtures to fasten lump components. Additional strategies have been identified but their application has been found out of reach of the technology developers, such as the increase in the manufacturing batch size or the inclusion of solvent recycling, which can only be implemented at larger manufacturing scales. Further strategies, such as the use of recycled Si wafers or dry adhesives as fixtures, represent future solutions for the reduction in the environmental impact which require further R&amp;D efforts from different disciplines. This highlights the need for holistic and multidisciplinary research efforts to fully achieve the circular design.

Quantitative Distributions of Product Ions and Reaction Times with a Binary Mixture of VOCs in Ambient Pressure Chemical Ionization.

A model to quantitatively predict ion abundances from atmospheric pressure chemical ionization (APCI) between hydrated protons and a volatile organic compound (VOC) was extended to binary mixtures of VOCs. The model includes differences in vapor concentrations, rate coefficients, and reaction times and is enhanced with cross reactions between neutral vapors and protonated monomers. In this model, two specific VOCs were considered, a ketone, 6-methyl-5-hepten-2-one (M, and an amine, 2,6-di-tert-butyl-pyridine (N), with measured "conditional rate coefficients" (in cm3·s-1) of kM = 1.11 × 10-9 and kN = 9.17 × 10-10, respectively. The cross reaction of MH+(H2O)x to NH+(H2O)y was measured as kcr = 1.31 × 10-12 at 60 °C. Cross reactions showed an impact on ion abundances at t > 30 ms for equal vapor concentrations of 100 ppb for M and N. In contrast, this impact was negligible for vapor concentrations of 1 ppb and did not exceed 5% change in product ion abundance up to 1000 ms reaction times. The model was validated with laboratory measurements to within ∼10% using an ion mobility spectrometer and effective reaction time obtained from computational fitting of experimental findings. This was necessitated by complex flow patterns in the ion source volume and was determined as ∼10.5 ms. The model has interpretative and predictive value for quantitative analysis of responses with ambient pressure ion sources for mass spectrometry and ion mobility spectrometry.

Dynamic Energy Analysis of Different Heat Pump Heating Systems Exploiting Renewable Energy Sources

Renewable energy source-fed heat pumps (HPs) may perform up to very high-efficiency standards, offering a promising tool in the wider residential heat decarbonization effort. In this context, this paper investigates different heating configurations utilizing various renewable thermal sources in conjunction with an HP-based system in order to determine the optimal configuration in terms of efficiency, using an existing, fully functioning residential building in Zaragoza, Spain, as our case study, comprising 40 dwellings. Four different HP configurations are investigated:, (i) an air-source system, (ii) a ground-source system, (iii) a dual-source system with solar thermal collectors, and (iv) a triple-source system based on solar, geothermal, and ambient sources. For the purpose of such investigation, detailed dynamic energy simulations are conducted through the use of the INTEMA.building tool (developed in Modelica), applying a multi-objective optimization process that aims at minimizing both the annual electricity consumption and the net present cost. It is demonstrated that the renewable thermally driven HPs are more efficient than the conventional, air-source ones, with the seasonal coefficient of performance increasing by 9.98% (ground source), 4.57% (dual source), and 17.40% (triple source), compared to the air-source heat pump system. Finally, it is revealed (via integrated techno-economic analyses) that the most effective and economical design is the dual source system, while the most expensive is the ground-source configuration. These findings can guide the ongoing design efforts on green residential heat solutions at both research and commercial implementation level.

Evaluation of Dynamic Properties of Trees Subjected to Induced Vibrations

The preservation of trees in urban and archeological areas is a theme of particular relevance. Modern systems of monitoring, together with approaches for deriving the main characteristics of trees influencing their response toward extreme events, are nowadays at the basis of a growing number of studies. The aim of the present paper is the dynamic identification of trees carried out by employing an approach which combines a simple data-acquisition system, direct and ambient sources of excitation, and different data-processing methods. In particular, using a single accelerometer placed at different sections of the trunk and considering excitations induced by either pulling tests or ambient vibrations, the derivation of the main frequencies and levels of modal damping characterizing the dynamic response of a sour cherry tree (Prunus cerasus) is carried out. A finite element model of the tree is also carried out to support the validation of the proposed approach and further analyze the derived outcomes. The obtained results underline the feasibility of the proposed approach in deriving information useful for assessing the behavior of trees toward dynamic actions and, consequently, of particular relevance for the identification of possible damages induced by variations in terms of dynamic characteristics (frequencies) and damping.

Effect of paper filter windows on indoor exposure to particles of outdoor origin

Buildings are often located near ambient air pollution sources such as wildfire or heavy traffic areas. While windows in buildings are intermittently open for free cooling or natural ventilation, increased leakage area can lead to elevated human exposure to air pollutants of outdoor origin. The objective of this study is to investigate the effectiveness of paper filter windows in reducing exposure to outdoor air pollution and improving indoor air quality. The physical properties of paper windows as filtration media were experimentally determined, based on which multi-zone indoor air quality and ventilation analysis (CONTAM) simulations were performed for a full-scale building. The results show that the outdoor-indoor air exchange rate of a building can increase about 100% when conventional windows are replaced with paper filter windows. Even with the increased air exchange rate, the infiltration of outdoor particles into the building was reduced about 57–77% for the particle size range of 7–300 nm. These findings imply that paper windows have potential benefits for controlling both outdoor originated pollutants and indoor-generated pollutants with minimal energy inputs, especially in cities and communities impacted by urban air pollution and wildfires.

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

AbstractEffective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management.This article is categorized under: Engineering Water &gt; Methods Engineering Water &gt; Sustainable Engineering of Water Science of Water &gt; Water Extremes

Characteristics of the Oceanic Ambient Seismic Noise Around Tristan da Cunha in the South Atlantic From OBS Data

AbstractIt has long been recognized that specific ambient seismic noise is generated within the ocean. Therefore, ocean bottom seismometers have advantages to reveal the noise characteristics directly in the deep sea. We use the seismic recordings around Tristan da Cunha collected by 20 OBSs and two island stations to investigate the ambient noise sources in the middle of the South Atlantic. We analyzed the power spectral density, ambient noise cross‐correlations, and correlations with wave heights model. While primary microseisms (∼10–20 s) in this region are deficient, the secondary microseisms are strong and dominate the ambient noise at periods of ∼1–10 s, with the peak broadening to shorter periods (1–3 s). The secondary microseisms are strongly correlated with the local wave heights, especially at shorter periods (∼1–3 s), suggesting that the microseisms are affected mostly by local wave interactions. We observed clear Empirical Green's functions at periods of ∼3–10 s, indicating the existence of distant sources of secondary microseisms. Results of asymmetry analysis of cross‐correlations suggest the prominent direction of the distant source should be located to the Southwest and show barely seasonal variations. The correlations with wave heights indicate a possible source region in the southeast Pacific Ocean. Therefore, besides the dominating local wave‐wave interactions, the secondary microseisms around Tristan region are affected simultaneously by persistent distant sources from the deep basin in the Southeast Pacific Ocean, which might be related to the intense wave‐wave interactions caused by the blocking effect of the Drake Passage on ocean currents and waves.

Ion Heating in Advanced Dielectric Barrier Discharge Ion Sources for Ambient Mass Spectrometry.

Dielectric barrier discharges (DBD) are highly versatile plasma sources for forming ions at atmospheric pressure and near ambient temperatures for the rapid, direct, and sensitive analysis of molecules by mass spectrometry (MS). Ambient ion sources should ideally form intact ions, as in-source fragmentation can limit sensitivity, increase spectral complexity, and hinder interpretation. Here, we report the measurement of ion internal energy distributions for the four primary classes of DBD-based ion sources, specifically DBD ionization (DBDI), low-temperature plasma (LTP), flexible microtube plasma (FμTP), and active capillary plasma ionization (ACaPI), in addition to atmospheric pressure chemical ionization (APCI) using para-substituted benzylammonium thermometer ions. Surprisingly, the average extent of energy deposited by the use of ACaPI (90.6 kJ mol-1) was ∼40 kJ mol-1 lower than the other ion sources (DBDI, LTP, FμTP, and APCI; 130.2 to 134.1 kJ mol-1) in their conventional configurations, and slightly higher than electrospray ionization (80.8 kJ mol-1). The internal energy distributions did not depend strongly on the sample introduction conditions (i.e., the use of different solvents and sample vaporization temperatures) or the DBD plasma conditions (i.e., maximum applied voltage). By positioning the DBDI, LTP, and FμTP plasma jets on axis with the capillary entrance to the mass spectrometer, the extent of internal energy deposition could be reduced by up to 20 kJ mol-1, although at the expense of sensitivity. Overall, the use of an active capillary-based DBD can result in substantially less fragmentation of ions with labile bonds than alternate DBD sources and APCI with comparably high sensitivity.

Real-Time Monitoring of Reactions by Headspace Sampling under Ambient Mass Spectrometric Conditions

The helium-plasma (HePI) ionization mass spectrometry method was used to track the formation of reaction products and consumption of reactants by continually monitoring the vapors in the reaction headspace in real time. Two examples of the application procedure are presented here: a nucleophilic acyl substitution reaction and a 1,4-nucleophilic addition process. For the purposes of the work, a specially constructed helium-plasma ionization (HePI) source was mounted on a single-quadrupole benchtop mass spectrometer. However, any electrospray source is easily convertible to an ambient HePI source. This setup has the potential to be used in the teaching of organic and analytical chemistry laboratories, as well as for demonstrations. Because the procedure requires only the headspace of the reaction vessel to be sampled by an easily prepared Venturi suction device (pump), the experiments can be quickly set up and adapted for any available mass spectrometer equipped with an electrospray source. For sampling, students can be shown how to prepare their own Venturi pumps and conduct reactions at different temperatures.

Differential patterns of association between PM1 and PM2.5 with symptoms of attention deficit hyperactivity disorder

Few studies have evaluated the association between ambient particulate matter (PM) exposure and childhood attention deficit hyperactivity disorder (ADHD). We explored associations between long-term exposure to ambient PM1 and PM2.5 (particles with airborne diameters of <1 µm and <2.5 µm, respectively) with positive screening for ADHD among 164,081 school-aged children in China. Parents or guardians completed a checklist using DSM-IV, and PM1 and PM2.5 exposures linked via residential addresses were estimated using a spatial statistical model. Long-term exposure to higher ambient PM1 (odds ratio = 1.74; 95% confidence interval = 1.47–2.06 per 10.0 μg m–3) and PM2.5 (odds ratio = 1.65; 95% confidence interval = 1.45–1.88 per 10.0 μg m–3) was associated with greater odds of screening positive for ADHD. The associations were heterogenous across regions, with stronger associations for PM1 exposure than for PM2.5. Our findings highlight the potential importance of ambient PM mass concentrations, sizes, components and sources for protecting children’s neurological health in China, and in the design of interventions to decrease the health burden of children’s ADHD. This large study, conducted in school-aged children in China, evaluates the relationship between long-term exposure to ambient particulate matter and positive screening for attention deficit hyperactivity disorder (ADHD). The authors report stronger associations between ADHD and PM1 exposure than between ADHD and PM2.5.

38 Physicochemical Characterisation and Potential Health Effects of Tyre Wear Particles

Abstract Tyre wear is an increasing source of ambient particulate matter pollution in the UK and around the world. Currently, tyre wear accounts for 11% of total particulate matter (PM2.5) in the UK, and over the next 10 years that figure is projected to steadily increase, with UK brake and tyre emissions projected to reach 6 kilotonnes by 2030. Being able to reliably identify and monitor tyre wear PM in ambient sources is of increasing importance due to its potential human health effects. Here we present a series of characterisation experiments on tyre particles, size-fractionated using a dynamometer. Chemical and elemental composition has been determined by inductively coupled plasma mass spectrometry (ICP-MS) and two dimensional gas chromatography time of flight mass spectrometry (GCxGC-TOF-MS), with particle morphology studied using a scanning electron microscope and energy dispersive spectroscopy (SEM-EDS) and an electrical low pressure impactor (ELPI). The potential health effects of these particles have then been assessed in vitro using human bronchial epithelial cells following the IARC characteristics of carcinogenicity, specifically looking at (pro)inflammatory cytokines, cytotoxicity and mitochondrial function. Tyre wear particles have the ability to reduce mitochondrial function significantly from doses as low as 5μg/ml after 24 hours, with the induction of cytokines and chemokines currently being profiled. Additionally, a representative elemental and chemical fingerprint of tyre rubber has now been established. By understanding the composition and morphology of these particles, we can begin to detect them in atmospheric samples, and using in vitro models, understand their potential health effects.

High-Efficiency ZnS: Cu+, Al3+ Scintillator for X-ray Detection in a Non-Darkroom Environment.

Scintillator is a key component in X-ray detectors that determine the performance of the devices. Nevertheless, due to the interference of the ambient light sources, scintillators are only operated in a darkroom environment currently. In this study, we designed a Cu+ and Al3+ co-doped ZnS scintillator (ZnS: Cu+, Al3+) that introduces donor-acceptor (D-A) pairs for X-ray detection. The prepared scintillator displayed an extremely high steady-state light yield (53,000 photons per MeV) upon X-ray irradiation, which is 5.3 times higher than that of the commercial Bi4Ge3O12 (BGO) scintillator, making it possible in X-ray detection with the interference of ambient light. Furthermore, the prepared material was employed as a scintillator to construct an indirect X-ray detector, which performed a superior spatial resolution (≈10.0 lp/mm) as well as persistent stability under visible light interference, demonstrating the feasibility of the scintillator in practical applications. Therefore, this research presented a convenient and useful strategy to realize X-ray detection in a non-darkroom environment.

Machine learning-based electro-magnetic field guided localization technique for autonomous underwater vehicle homing

Precise guidance is the primary requirement for the reliable homing of autonomous underwater vehicles (AUV). The necessity for accurate short-range position tracking arises due to the limitations of the inertial sensors’ cumulative integral drift, unreliable acoustic positioning system in very short ranges, and non-propagation of high-frequency electromagnetic signals of the GPS system. In this article, a very low-frequency electro-magnetic (EM) field is generated for very short-range guidance for the homing operation of underwater vehicles. The design and modeling of the 3-axis low-frequency EM-based shallow water submerged Docking Station (DS) at 10 m depth is explained and the EM field is generated for a 20 m range in the 3-axis plane using two perpendicular coils operated in distinct frequencies. The stochastic magnetometer measurement model is used to estimate the relative kinematics of the underwater vehicle with respect to the EM docking coil. The EM field datasets generated from EM field maps around DS are used for training and validating with different Machine Learning (ML) algorithms. The designed ML aided EM-based tracking technique guidance algorithms are validated for underwater vehicle localization that requires a precise and reliable homing process to the DS even in turbid waters, bio-fouling environment, and reflective boundaries though it has limitations in presence of ambient ferromagnetic sources. Numerical simulations have been carried out with conventional methods and ML algorithms on the generated EM data. The results indicate that the position accuracy achieved is within 1% for ML-aided EM based guidance techniques when compared to the conventional algorithm which has a position accuracy of 3%. ML aided techniques are advantageous as they are independent of the dynamic ocean environment and the underwater vehicle configuration and the Time to Home (ToH) to the DS is simulated in different scenarios, is 160 s–200 s with a vehicle speed of 0.25 m/s. It is observed that the vehicle can choose the shortest path for homing operation with the measured EM field values and with aid of ML techniques.

Symbol Detection of Ambient Backscatter Communications Under IQ Imbalance

Ambient backscatter communications (AmBC) have been mainly studied under the assumption of perfect inphase/quadrature-phase (IQ) balance, which is hard to achieve in practical AmBC networks. In this paper, we study the symbol detection in AmBC under IQ imbalances. In particular, considering a differential encoding scheme at the AmBC transmitter and an energy difference based detector at the AmBC receiver, we derive the bit error rate (BER) and the near-optimal detection threshold in closed forms. Since the near-optimal detection threshold requires a priori knowledge of the channel state information, the IQ imbalance parameters, the noise power and the transmit power of the ambient source, which is not readily known by the AmBC receiver, we propose a simple but practical method to estimate the near-optimal detection threshold by exploiting the received signal samples at the AmBC receiver. Simulation results validate the accuracy of the derived BER and detection threshold, and reveal that the studied detector is more robust to the amplitude imbalance than to the phase imbalance.

Triple‐band metasurface absorber for RF energy harvesting applications

AbstractEnergy harvesting is expected to be ubiquitous and essential to modern‐day life in the future. The limitations of ambient sources, such as thermal, mechanical, and solar, for energy harvesting can be overcome by RF energy harvesting for low‐power devices. With the help of perfect metasurface absorbers, an electromagnetic wave can be used as an ambient source for energy harvesting. In this paper, a simple polarization‐insensitive triple‐band perfect metasurface absorber is proposed for RF energy harvesting applications. The three‐layer (metal–dielectric–metal) meta‐atom of the designed metasurface consists of a cross‐shaped metal patch surrounded by slotted square‐shaped metallic patches and of meta‐atoms. The absorber is typically developed in S‐ and C‐bands with resonant frequencies of 3.06, 3.9, and 5.97 GHz. The absorption is near unity which persists over a wide angle of incidence. The experimental results are in close agreement, within 2%, with the simulation results. The reliability of the proposed absorber has been determined for the RF energy harvesting applications, and a high harvesting efficiency of 94.4% and 84% has been observed at 3.07 and 5.97 GHz, respectively.

A wide-band electromagnetic energy harvester

A basic-configured electromagnetic energy harvester is proposed. It operates with varying magnetic induction at the center of a coil. Two magnets are located stable at the center of a coil having a spacer between them. Harvester does not give any elasticity problem with spring-free structure. The importance of coefficient of recovery is explained in theory of harvesters for the first time to our knowledge. The airgap formed by the spacer varies due to the vibration and this generates an electromotive force. It is proven that the proposed harvester generates a voltage amount Vrms = 2.41 mV and an electrical power P = 17 μW under the vibration frequency f = 60 Hz with 1 mm displacement. It corresponds to power density of 0.358 μW/cm3. Output voltages show a maxima with Vrms = 2.41 mV under R = 0.35 Ohm with a decaying trend with frequency. For R = 0.7 Ohm, higher voltages can be generated till 650 Hz. It is a superiority that this device has a very wide power harvesting regime from 2 Hz to 1.5 KHz. With such a wide characteristics, it can be used for many applications to harvest continuous energy from random vibrating ambient sources with its cheap and basic structure.

Acoustic full waveform inversion for 2-D ambient noise source imaging

SUMMARYWe present a method for estimating seismic ambient noise sources by acoustic full waveform inversion (FWI) of interstation cross-correlations. The method is valid at local scales for laterally heterogeneous media, and ambient noise sources confined to the Earth’s surface. Synthetic tests performed using an actual field array geometry, are used to illustrate three unique aspects of our work. First: the method is able to recover noise sources of arbitrary spatial distribution, both within and outside the receiver array, with high fidelity. This holds true for complex velocity models and does not require a good initial guess for inversion, thereby addressing an outstanding issue in the existing research literature. Second: we analyse the extent of biases in source inversion that arise due to inaccurate velocity models. Our findings indicate that source inversion using simplified (e.g. homogeneous) velocity models may work reliably when lateral variations in velocity structure are limited to 5 or 10 per cent in magnitude, but is vitiated by strong variations of 20 per cent or higher, wherein the effect of strong scattering and/or phase distortions become significant. Finally, our technique is implemented without the adjoint method, which is usually inextricably linked to FWI. Inversions are performed using source kernels computed for each receiver pair, and this approach is computationally tractable for real-world problems with small aperture seismic arrays.

Characterizing sources and ozone formations of summertime volatile organic compounds observed in a medium-sized city in Yangtze River Delta region

Volatile organic compounds (VOCs) emitted from various sources into atmosphere could cause serious O3 pollution in urban areas. Although characterizations of ambient VOCs have been extensively studied in megacities, they are scarcely investigated in medium/small-sized cities, which could present different pollution characterizations due to the factors like emission sources and populations. Herein, field campaigns were conducted concurrently at six sites in a medium-sized city of Yangtze River Delta region to determine ambient levels, O3 formations and source contributions of summertime VOCs. During the observation period, the total VOC (TVOCs) mixing ratios ranged from 27.10 ± 3.35 to 39.09 ± 10.84 ppb at six sites. The ozone formation potential (OFP) results showed that alkenes, aromatics and oxygenated VOCs (OVOCs) were dominant contributors, together sharing 81.4% of total calculated OFPs. Ethene ranked the largest OFP contributor at all six sites. A high VOC site, KC, was selected as a case to detailed analyze diurnal variations of VOCs and its relationship with O3. Consequently, diurnal patterns varied with VOC groups, and TVOC concentrations were lowest during strong photochemical period (15:00–18:00 p.m.), opposite to the O3 peak. VOCs/NOx ratios and observation-based model (OBM) analysis revealed that O3 formation sensitivity was primarily in transition regime in summertime and that the reduction of VOCs rather than NOX would be more efficient to suppress O3 peak at KC during pollution episode. Additionally, source apportionment conducted with positive matrix factorization (PMF) indicated that industrial emission (29.2%–51.7%) and gasoline exhaust (22.4%–41.1%) were major sources for VOCs at all six sites, and that VOCs from industrial emissions and gasoline exhaust were the key precursors for ozone formation. Our results shed light on the importance of alkenes, aromatics and OVOCs in forming O3 and propose that preferentially reducing VOCs especially those from industrial emission and gasoline exhaust would benefit alleviating O3 pollution.