**5. Conclusion**

Significant progress has been made in the modeling of protein digestion and absorption in gastrointestinal tract since the first works on this topic in the beginning of the XX century. A multitude of methods of various complexity, biological significance, animal requirements and human applicability now exist to answer the question on digestibility of proteins and peptides. The degree of biological processes imitation ranges from single luminal enzymes in simulated gastric or intestinal fluid model to sophisticated devices, which take in account grinding, mixing and turbulence forces and transition times, as well as isolated animal organs.

There is, however, no single method, which will suit every possible research goal. The problem is, that as the biological significance increases, time and labor requirements also rise tremendously, and methods became progressively less suitable for high throughput screening, which is often required. In addition to this, there is no experimental model, which could imitate every last component of the digestion process. The enzyme-only models and organ fragments ignore mechanical forces, digestive devices lack intracellular hydrolases and transport steps, isolated organs usually represent only one compartment of the whole system, and they fail to exert mechanical forces as well. As there is no efficient methodology of sampling of the digested products after they are absorbed, but before they enter blood or lymph, whole animal models are also limited – they can only answer questions, whether a protein was absorbed and if it was suitable for the sustaining normal growth of an organism. A very promising approach has appeared with the development of the highperformance computation methods, that is, molecular modeling. Theoretically, mathematical modeling is able to join data on the digestion patterns of various proteins from *in vitro* experiments with the results of *in vivo* tests of the ability of an organism to

one picture (*cf.,* for example, the discussion in the work by Sterchi et al. (Sterchi & Woodley

The aim of a substantial part of pharmacological studies of peptide- and protein-based therapeutics is often the evaluation of the disintegration, dissolution and drug release profiles of oral drug formulations (Vardakou et al. 2011). Thus another aspect of food digestion becomes important – the mechanical grinding and mixing forces, exerted on a pharmaceutical formulation by various digestive tract compartments. To simulate such conditions devices of various complexity are employed (Schasteen et al. 2002; Vardakou et al. 2011). The applicability of such devices for the drug form disintegration testing is unquestionable, however, usually they reproduce only the luminal phase of food digestion. Thus the evaluation of the ability of released protein or peptide to get to the blood flow

A short reference of available method types is provided in table 2. It should be noted, that some *in vitro* methods could be combined and expanded to provide more relevant data. For example, single-enzyme digestions by stomach and small intestine could be performed sequentially, the Caco-2 cell culture is often overlaid by a simulated gastric fluid and possibly mucin (to protect cells and imitate the similar layer present *in vivo*). The computational methods are the most adaptive and powerful ones, however, to date their usability is limited to large screening studies due to huge preparatory experimental work.

Significant progress has been made in the modeling of protein digestion and absorption in gastrointestinal tract since the first works on this topic in the beginning of the XX century. A multitude of methods of various complexity, biological significance, animal requirements and human applicability now exist to answer the question on digestibility of proteins and peptides. The degree of biological processes imitation ranges from single luminal enzymes in simulated gastric or intestinal fluid model to sophisticated devices, which take in account grinding, mixing and turbulence forces and transition times, as well as isolated animal

There is, however, no single method, which will suit every possible research goal. The problem is, that as the biological significance increases, time and labor requirements also rise tremendously, and methods became progressively less suitable for high throughput screening, which is often required. In addition to this, there is no experimental model, which could imitate every last component of the digestion process. The enzyme-only models and organ fragments ignore mechanical forces, digestive devices lack intracellular hydrolases and transport steps, isolated organs usually represent only one compartment of the whole system, and they fail to exert mechanical forces as well. As there is no efficient methodology of sampling of the digested products after they are absorbed, but before they enter blood or lymph, whole animal models are also limited – they can only answer questions, whether a protein was absorbed and if it was suitable for the sustaining normal growth of an organism. A very promising approach has appeared with the development of the highperformance computation methods, that is, molecular modeling. Theoretically, mathematical modeling is able to join data on the digestion patterns of various proteins from *in vitro* experiments with the results of *in vivo* tests of the ability of an organism to

1980a)).

intact requires additional methods.

**5. Conclusion**

organs.

utilize these proteins. However, a vast array of preliminary data is required for optimization and validation of such models, and so they are relatively rare.

Thus, when considering various methods of protein digestibility assay, one should first of all define the limitations of the research at hand. If a high throughput screening is required? If there is a need for any data on digestion products? If absorption efficiency could vary and is essential? If a protein should serve as food or not? A possible decision tree for method optimization is presented on figure 1, albeit it should be considered more as a reference than a rule.

Fig. 1. A decision tree for protein or peptide digestibility assay choice


**1. Introduction**

Although the cause of the growing prevalence of allergic diseases remains unclear, the Old Friends Hypothesis (also called the Biodiversity Hypothesis) has recently been proposed [1]. The hypothesis states that the development of many diseases, including allergy, might be attributed to a lack of exposure to the "right" bacterial strains. Assuming this to be the case, it may be possible that identifying such bacteria could halt the allergic march and foster the development of novel prevention and treatment methods for allergic diseases.

The composition of the human microbiome is not only characteristic of each individual, but also evolves over time [2–7]. In various parts of the child's body, the microbiome begins to form and differentiate within the first six weeks post natum [2,3]. The most pronounced changes in the gut microbiome are usually observed during first two or three years of

**Citation:** Ło´s-Rycharska, E.; Goł˛ebiewski, M.; Sikora, M.; Grzybowski, T.; Gorzkiewicz, M.; Popielarz, M.; Gawryjołek, J.; Krogulska, A. A Combined Analysis of Gut and Skin Microbiota in Infants with Food Allergy and Atopic Dermatitis: A Pilot Study. *Nutrients* **2021**, *13*, 1682. https://doi.org/10.3390/nu13051682

Academic Editor: Carla Mastrorilli

Received: 24 March 2021 Accepted: 12 May 2021 Published: 15 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

human life [4,5,8]. Following this, the composition of the gut microbiome remains relatively stable, unless significant environmental changes occur [5]. The early gut microbiome is believed to be shaped by a range of factors, including maternal race-ethnicity, age, diet, antibiotic treatment, marital status, the mode of delivery, health complications after birth, infant feeding method, solid introduction, probiotics intake, exposure to tobacco smoke and pets [2,4,6,7].

Similarly, the skin microbiota evolves over the years according to changes in skin structure and function [9], becoming similar to that of adults by the age of 12–18 months [10]. A possible critical window during which microbial-based intervention would be possible is early infancy (i.e., the first six months of life) [10–12]. The composition of skin microbiota is site-specific and, again, depends on a range of factors, including local conditions such as invaginations, pockets, niches and surfaces, as well as moisture, sebum secretion, temperature and exposure to external factors [9,10]. In the neonatal period, the mode of delivery appears to play an important role in shaping the skin microbiome [2]. The microbiome, considered a complex biocenosis where various organisms interact, influences the immune system in a multifaceted way, modulating both innate and adaptive immunity, and shaping allergy development through interactions with the host genome [13–15].

The gut and the skin are crucial barriers through which environmental factors interact with the human body; the two compartments are also massively colonized with distinct microbial communities. Furthermore, they constitute complex immune organs that are fully integrated into the overall immune system. Proper skin and gut functioning is essential for homeostasis, and decreases the risk of allergy. Hence, exposure to the many environmental factors that shape the microbiome of the gut and skin epithelium can alter the epithelial surfaces and thus drive the type 2 immune response, which is known to underlay most, if not all, atopic diseases. Many studies indicate the existence of a gut–skin axis, which is modulated by microbiota and its metabolites [16–18].

Generally, the first symptoms of allergy involve the skin and/or the gastrointestinal tract [19], manifesting as atopic dermatitis (AD) and/or food allergy (FA). These disorders are difficult to diagnose in early infancy, as there are no specific markers, and diagnosis is based mainly on their symptoms. Although previous studies have yielded varying results, it has been found that gut microbiota differed between otherwise healthy subjects and those who were food sensitized (FS) or food allergic (FA) [20,21]. It has been shown that the composition of the microbiota in the neonatal period may be associated with the development of allergies up to the age of one year. In later life, alpha-diversity, betadiversity, and the richness of individual bacteria are shaped differently in non-allergic and allergic children [3]. It has also been shown that the microbiome structure at three months is associated with the development of sensitization up to one year of age [11]. It also appears that the microbiome of the gut in infancy is a prognostic factor of allergy resolution by eight years of age [12].

The bacterial community structure has also been found to differ between IgE-mediated and non-IgE-mediated FA children [22], and differences can be seen between the microbiota associated with specific food allergies [23–25]. Fieten et al. report no differences in microbial diversity between FA and non-FA children; however, a fecal microbial signature that discriminated between the presence and absence of FA was found in children with AD [26].

Similarly to the gut microbiome, the skin microbiota has also been found to differ between patients with AD and those without [9], and other studies have reported decreased skin microbiome diversity in AD patients [16,27]. The significance of the skin microbiome in AD pathogenesis was confirmed by Laborel-Preneron et al., who showed that *Staphylococcus aureus* colonizing atopic skin promoted inflammation in children aged one to three years, while commensal *S. epidermidis* strains might mitigate this effect [28]. Similarly, Kong et al. found that the abundance of *Staphylococcus* (particularly *S. aureus*) was greater in children with AD, and that microbial diversity increased over the course of treatment [29]. Interestingly, while the development of AD appears to be related to colonization by *S. aureus*, it has also been proven that exacerbations of skin lesions are connected to a reduction

in skin microbiome diversity, while improvement is not directly related to a reduction in *S. aureus*, but rather to an increase in bacterial diversity [16]. In addition, prospective studies have identified changes in gut and/or skin microbiota in children, even prior to the development of food sensitivity (FS), FA or AD, with changes being apparent as early as in the third month of life [11,16,30–33]. It has also been proposed that decreased *Staphylococcus* richness in the skin microbiome during early infancy might be a prognostic factor of AD development before 12 months of age, and that colonization by commensal *Staphylococci* might protect against eczema [32].

Current evidence suggests that while the gut microbiota may demonstrate a reduction in microbial diversity before AD onset, no such relationship has been noted for the skin microbiota [16,23]. Furthermore, although gut dysbiosis has been described in AD [23,27,34], it is not clear whether the modulation of gut microbiota can impact skin microbiota and vice versa. The role of the skin microbiome in FA development is also unknown.

To gain a more holistic understanding of how the microbiome of the human body interacts with the immune system, and how it can influence the development of FA and/or AD, it is necessary to study the skin and gut together as part of one combined study. The present study is the first such study to compare the microbiota of two compartments, i.e., gut and skin, in young infants with early onset of FA and/or AD. Indeed, of all the studies regarding the relationship between microbiota and allergy published to date, only one [35] has analyzed multiple compartments.

The present study characterizes the alpha and beta diversity of the fecal, i.e., gut, and skin bacterial communities from infants with FA and/or AD and compares them with those of healthy control subjects.

We hypothesize that:

**Hypothesis 1.** *Gut and skin alpha diversity differ between children with an allergy (FA and/or AD) and those without;*

**Hypothesis 2.** *The communities within a single compartment differ according to the clinical status of the child;*

**Hypothesis 3.** *OTUs characteristic for each compartment-clinical status communities exist;*

**Hypothesis 4.** *Relationships exist between the gut and skin microbiota, and these differ with regard to clinical status.*

To confirm these hypotheses, V3-V4 16S rRNA gene libraries were generated and sequenced, and the resulting sequences were analyzed bioinformatically.


5. Conclusion

The filtering properties of the 1D hybrid heterostructure built according to the GTM and GF sequences are investigated in this study. It was observed that the two common quasiperiodic sequences exhibits a multitude of channels with zero

transmission for specific values of parameters m and n. In particular, the spectrum of GTM system possesses similar narrow gaps without oscillation beams at a given parameter: m = 2 pm. Indeed, a sharp transmission peak is appreciated in the whole frequency range whose positions are sensitive to superconductor temperature. Therefore, the considered system can be useful as a selective pass band multichannel filter whose narrow bandwidth can be adjusted by temperature. In addition, the main GTM system gives staking gaps which are enhanced by applying a specific deformation. Similarly, the GF heterostructure suggests an identical channel frequencies without transmission as compared to GTM system but their spectrum have particular oscillations around the cut-off frequency. Thus, the properties of filtering change by modifying the type of sequences and the parameters of constituent materials.


**Gökhan Kodalak** is a theorist, teaching philosophies of architecture, nature, and cities at Pratt Institute; an architect, directing design studios at Parsons School of Design; and an architectural historian, having completed his PhD dissertation titled "Spinoza and Architecture" at Cornell University. Kodalak's work explores diverse issues such as architectural ontology and epistemology, design ecology and nature-architecture continuum, spatial politics and urban commons, affective aesthetics and immanent ethics, and the heterodox Spinozist conception that architectural modalities are alive [*animata*]. His scholarship has been awarded by the Andrew W. Mellon Foundation and Canadian Centre for Architecture, and published in *Footprint* (2015), *Spinoza's Philosophy of Ratio* (2018), *Deleuze Studies Journal* (2018), *Log* (2020-21), and *Architectures of Life and Death* (2021). And his design work is acknowledged with international awards and exhibited at Johnson Museum of Art (NY) and Antalya Architecture Biennial.

**Thijs Lijster** is assistant professor of philosophy of art and culture at the Faculty of Arts of the University of Groningen, and was a postdoc researcher at the Culture Commons Quest Office of the University of Antwerp. He published *Benjamin and Adorno on Art and Art Criticism*. *Critique of Art* (2017), was editor of *The Future of the New* (2018), and coeditor of *De Nieuwe Duitse Filosofie* (*The New German Philosophy*, 2013), *De Kunst van Kritiek* (*The Art of Critique*, 2015), and *Spaces for Criticism*. *Shifts in Contemporary Art Discourses* (2015).

**Lara van Meeteren** lives and works in Hong Kong and Bangkok. She conducts research, writes and co-organises projects. Aiming to make the political nature of art and its events and institutions visible, in her work Lara explores the idea of contemporary art as counter-hegemonic practice. Her writing has been published in Open Philosophy, OnCurating and Social Inclusion, among others. With co-author Bart Wissink, she has a collaborative research-based practice, interrogating the social role of art and independent art organising in East and Southeast Asia. A recent ongoing project, with Ariane Sutthavong and Sina Wittayawiroj, is the *inappropriate BOOK CLUB*, a common-based initiative focusing on art and politics.

**Hanka Otte** is senior researcher and university teacher in the field of art sociology and cultural policy. She works at the University of Antwerp (BE), Groningen University (NL) and as a freelancer. In her research, she mainly focuses on the civil value of art and how policy can help generate culture commons. In 2018 she was awarded the NWO Boekman Dissertation Prize for her thesis *Binding or Bridging? On the Relation between Art, Cultural Policy and Social Cohesion*. Hanka lives in Groningen, where she is a member of Breeding place De Campagne, an organisation figuring out how to 'common'.

**Ching Lin Pang**  is Associate Professor in the Department of Applied Linguistics, Translation and Interpreting and Academic Director of the Postgraduate Program China Europe Cultural Curatorship Studies, University of Antwerp. She is also affiliated with KU Leuven, Belgium. Her research is situated at the intersection of mobilities, multilingualism, arts and aesthetics of cultural workers in the urban context. She is an editorial member of Leuven University Press and the Cosmopolitan Civil Societies Journal. Contact<EMAIL_ADDRESS>
**Tian Shi** recently obtained her PhD in anthropology from KU Leuven, Belgium. Her research interests include research on migration, refugee resettlement, ethnic identity, and the Hmong diaspora. She has been involved in a variety of migration and new media research projects, such as 'Ethnic Policy in the EU and European Hmong Integration' (CPRC project).

**Stavros Stavrides**, architect and activist, is Professor at the School of Architecture, National Technical University of Athens, Greece. He is currently the head of NTUA *Lab for the Architectural Design and Communication.* He has done extensive research fieldwork in Brazil, Uruguay, Argentina and Mexico focused on housing-as-commons and urban struggles for self-management. His recent publications include *Common Spaces of Urban Emancipation* (Manchester 2019), *Common Space. The City as Commons*, (London 2016, Istanbul 2016, Athens 2019, Lisbon 2021, Milano 2022, Seoul and Belgrade forthcoming) and *Towards the City of Thresholds* (Trento, 2010, Madrid 2016, Istanbul 2016, N. York 2019). Personal homepage: http://courses.arch.ntua.gr/ stavrides.html.

**Maria Francesca De Tullio** is researcher in Constitutional Law (Federico II University of Naples). She also worked at University of Antwerp, within the project Cultural and Creative Spaces and Cities (www.spacesandcities.com), and fulfilled a research residency at Université Paris 2. She authored a book on *Substantial Equality and New Dimensions of Political Participation*. Other main areas of research are: counter-terrorism and states of emergency, competition law on the Internet, the collective dimension of privacy in the age of big data. Finally, she is active in co-research processes with Italian and European commons movements, also in their negotiations with administrations.

**Louis Volont** is a sociologist. He is a Fulbright visiting scholar at MIT's School of Architecture & Planning (Program in Art, Culture & Technology) and a postdoc at the HafenCity University Hamburg. He studied sociology in Leuven and Milan, and defended his PhD in 2021 at the University of Antwerp (*Shapeshifting: The Cultural Production of Common Space*, Antwerp University Press). Louis investigates processes of urban future-making, urban commoning, socio-technical imaginaries and the work of Henri Lefebvre. His work has been featured in journals such as *Antipode*, *City & Community* and *Social Inclusion*.

**Bart Wissink** is associate professor urban studies and urban policy at City University of Hong Kong. His research centres on issues relating to urban governance and social justice, with a focus on cultural commoning in recent years. With co-author Lara van Meeteren, he has a research-based practice that interrogates the social role of art and independent contemporary art organising in East and Southeast Asia. They co-created *coming soon •* เร็ว ๆ นี ้ for the 2018 Bangkok Biennial (www.comingsoonbkk.com). For the 2020 Bangkok Biennial, together with Ariane Sutthavong and Sina Wittayawiroj, they organised the *inappropriate BOOK CLUB*, a common-oriented initiative focusing on art and politics (www.facebook.com/inappropriateBOOKCLUB).

## ABOUT THE SERIES URBAN NOTEBOOKS

The series Urban Notebooks/Stadsschriften/Cahiers Urbains treats subjects that deal with cities and urbanity in the large sense. It aims to stimulate the social debate about urbanity.

The series is directed by an interdisciplinary and interuniversity editorial committee, chaired by Eric Corijn (editor in chief).

www.aspeditions.be

## SUR LA SÉRIE CAHIERS URBAINS

La série Urban Notebooks/Stadsschriften/Cahiers Urbains accueille des sujets qui traitent de la ville et de l'urbanité au sens large du terme. Elle vise à stimuler le débat social au sein et au sujet de la ville.

Elle est dirigé par un comité de rédaction présidé par Eric Corijn (président).

www.aspeditions.be

## OVER DE REEKS 'STADSSCHRIFTEN'

De reeks Urban Notebooks/Stadsschriften/Cahiers urbains staat breed open voor onderwerpen die de stad en stedelijkheid onderzoeken en maatschappelijk in discussie brengen.


*3.2. In-situ Helium Implantation and Annealing in H2*

Helium bubble evolution was observed and characterized in-situ during implantation in an H2 environment at −100 ◦C. Figure 3a shows initial observation of 4He bubbles after implantation to a peak concentration of 6 at.%. All reported concentration values assume no escape of He from the TEM foil. Bubbles initially had low areal-density and were approximately 1.2 nm in diameter, similar to the microstructure that was observed in the preliminary experiments at room temperature. Larger bubbles were observed in some areas, but bubble nucleation was generally homogenously distributed and uniform in size throughout the implantation. Bubble density visibly increased with increasing implantation dose as shown in Figure 3b–f. Figure 4a shows the measured bubble diameter and density changes as a function of He concentration. Bubble size remained constant during the implantation, but areal bubble density was observed to increase with implantation time, starting at 8 × 1011 bubbles/cm2 in Figure 3a at 6 at.%, and reaching a density of 5 × 1012 bubbles/cm2 in Figure 3f at 23 at.%. Bubble

density change as a function of implantation dose was determined using a linear fit, where the slope = 2.7 × 1011 bubbles/cm2/at.% and the intercept = −1.2 × 1012 bubbles/cm2.

**Figure 3.** BF in-situ TEM images showing He bubble evolution during implantation at −100 ◦C under H2 gas flow from peak concentrations of (**a**) 6 to (**f**) 23 at.%.

**Figure 4.** Bubble size and density changes (**a**) during in-situ implantation at −100 ◦C in H2 gas, and (**b**) during annealing from −100 ◦C to −60 ◦C. Data points in (**a**) were measured from the images in Figure 3. Bubble diameter is shown as solid green circles, and bubble density is shown as empty black circles. Linear fits are shown for the data in (**a**).

After implantation, the sample was heated in-situ from −100 ◦C to 0 ◦C in H2 gas. According to Figure 1a, most H2 should be desorbed from the sample during this heating process. Bubble size and density were characterized during the annealing, when possible, and are summarized in Figure 4b. The sample was annealed to −60 ◦C in 390 s (0.10 ◦C/s), held at −60 ◦C for approximately 10 min, then ramped from −60 ◦C to 0 ◦C in approximately 3 min. Images recorded from −60 ◦C to 0 ◦C had too much dynamic contrast evolution for bubble size analysis, so two post-annealing images were analyzed and the results averaged to obtain a final density of 7 × 1012 bubbles/cm2 and a final diameter of 1.3 nm. No significant changes in bubble diameter were observed during annealing up to 0 ◦C. No significant changes in bubble density were observed under annealing from −100 ◦C to −60 ◦C, but the bubble density did appear to increase from 6 × 1012 bubbles/cm2 to 7 × 1012 bubbles/cm2 after

annealing from −60 ◦C to 0 ◦C (not shown in figure). Uncertainty is not provided for bubble density measurements because a single density value was calculated from each image.

#### **4. Discussion**

Helium bubble diameter was observed to remain constant during the cryogenic in-situ implantation in the presence of H2, but bubble density increased as a function of time. Even though β-phase hydride formation could not be identified within experimental error, the lower concentration α-phase hydride likely formed and could impact bubble evolution. Although H2 may influence the nucleation and growth of He bubbles in Pd, the H concentration is very low in α-phase (H/Pd = 0.03), so cryogenic implantation temperature is thought to be a more-likely dominating factor. Very little literature exists on He implantation in a H2 environment or on cryogenic He implantation, so this section will discuss, in relation to this work, (1) a comparison of this work with previous studies on He bubble evolution in Pd, (2) theory on He bubble growth at cryogenic temperature, and (3) how H-He interactions may affect the results.


**Electrospun Nanomaterials Applications in Food, Environmental Remediation, and Bioengineering**

Printed Edition of the Special Issue Published in *Nanomaterials* Ricardo Mallavia and Alberto Falco Edited by

www.mdpi.com/journal/nanomaterials

**Electrospun Nanomaterials**


2. Factsheet of magnesium

The salient characteristics including atomic and crystalline and chemical characteristics and physical, thermal, biological, and mechanical properties of magnesium are shown in Figure 1.


*Article* **Determination of Lidar Ratio for Major Aerosol Types over Western North Pacific Based on Long-Term MPLNET Data**

**Sheng-Hsiang Wang 1,2,\*, Heng-Wai Lei 1, Shantanu Kumar Pani 1, Hsiang-Yu Huang 1, Neng-Huei Lin 1,2, Ellsworth J. Welton 3, Shuenn-Chin Chang 4,5 and Yueh-Chen Wang 1**

Received: 10 July 2020; Accepted: 19 August 2020; Published: 26 August 2020

**Abstract:** East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (*Sp*), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean *Sp* values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The *Sp* results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that *Sp* was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The *Sp* for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the *Sp* values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.

**Keywords:** ground based remote sensing; aerosols optical properties; lidar ratio; aerosol type

#### **1. Introduction**

Atmospheric aerosols play a crucial role in governing the regional-to-global climate change. They influence the Earth-atmosphere energy budget directly by absorbing and scattering incoming (shortwave) and outgoing terrestrial (longwave) radiation ([1]) and indirectly by modifying cloud microphysical properties ([2]). However, uncertainties in their compositions, characteristics, size

distributions, concentrations, and vertical distributions throughout the atmospheric column make the exact quantification of their overall impact challenging. Several types of aerosols originate from different sources, and their physical, chemical, and optical characteristics also vary significantly ([3]). Such variations lead to large uncertainties in aerosol radiative forcing estimations. Information about vertical distributions of aerosols and their optical properties is of prime importance and crucial for quantifying the accurate radiative forcing ([4–6]). However, vertical aerosol distributions are unevenly distributed over the globe ([7–11]). Due to wide spatial and temporal variability, the understanding of the vertical structure of aerosols is still very limited [8].

The knowledge on vertical distributions—mostly gained worldwide—was from in-situ probing using rocket and balloon-borne instrumentations (e.g., [12,13]), ground-based lidar (light detection and ranging) measurements (e.g., [14–23]), and satellite remote-sensing ([24,25]). In-situ measurements on airplanes are sparse and passive remote-sensing only provides a coarse estimation of vertical aerosol distribution. However, lidar (an active remote-sensing instrument) is the most prominent tool for aerosol profiling in terms of the optical properties of aerosols ([26]). Lidar was used to characterize the vertical profiles during several field experiments, such as FIRE (First ISCCP Regional Experiment; [27]), INDOEX (Indian Ocean Experiment; [28,29]), SAFARI-2000 (Southern African Regional Science Initiative; [30]), ACE-2 (Aerosol Characterization Experiment; [31]); BASE-ASIA (Biomass-Burning Aerosols in South-East Asia: Smoke Impact Assessment; cf. http://smartlabs.gsfc.nasa.gov/; [32]), and 7-SEAS (Seven South East Asian Studies; [33]). Moreover, the variations of vertical profiles on the regional scale, such as in major cities, e.g., Los Angeles, Paris, Tokyo, and Hong Kong, have been extensively investigated by using lidar datasets ([22] and references therein).

In addition to sunphotometer (e.g., [34,35]) and passive satellite products (e.g., [36]), lidar measurements can also be used to classify different aerosol types (e.g., [37]). Measurements from depolarization lidars (ground and space-borne) can be used to separate between different aerosol types (e.g., [38,39]), and nowadays even for the calculation of aerosol number concentrations in the atmosphere ([26,40]). Among lidar measurements, lidar ratio (or extinction-to-backscatter ratio; *Sp*) particularly contains the information about aerosol types and provides insight into the size and absorption of aerosol particles. *Sp* generally depends on various factors, such as size, shape, and refractive index of aerosols (e.g., [19]) and relative humidity (RH). This can be computed as a function of wavelength of the laser light if the composition, shape, and size of aerosols are known ([40]). Spectral analyses of *Sp* combined with information from AERONET (Aerosol Robotic Network) inversion products serve to estimate aerosol type (e.g., [41]). Measurements of *Sp* combined with aerosol depolarization also serve for aerosol typing (e.g., [42–45]). Such information is also used in CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite data to classify the aerosol type through the use of inversion and processing *Sp*. This aspect has been greatly useful for observation and modelling for the global distribution of aerosols.

In recent years, Asian pollution (e.g., haze) has attracted worldwide attention. Aerosols mainly from anthropogenic activities in East Asia are recognized as an important source of regional and global pollution ([29,46]). Moreover, depending on meteorological conditions, this pollution also affects the air quality over downwind areas through long-range transport. Some studies reported that the aerosol types in East Asia (i.e., Taiwan) were large and highly variable in seasonal trends, influenced by different aerosol delivery mechanisms, and also impacted the radiation and air quality ([47]). Long-range transport of anthropogenic emissions from mainland China, dust events from the desert regions of northern China and Mongolia, and biomass-burning from peninsular Southeast Asia significantly impact the air quality over northern Taiwan ([23,47,48]). Over the past several years, most of the air quality studies over northern Taiwan relied on sampling, ground telemetry equipment to monitor observations, and model simulations (e.g., [49–53]). However, those studies are not sufficient to enable one to understand the impacts of air pollutants with accuracy due to the lack of understanding of vertical pollutant distributions. In addition to monitoring the transmitted light reaching the height of

the spatial structure of atmospheric pollutants, the novelty of surface telemetry tool technology is to overcome the lack of information on the vertical profile.

Therefore, this study quantitatively analyzed vertical aerosol distributions in the lower troposphere by examining the aerosol extinction profiles derived from MPL (micro-pulse lidar) measurements over a rural location in northern Taiwan. Long-term (2005–2012) data of vertical aerosol profiles and column-integrated aerosol optical properties from MPL and AERONET (Aerosol Robotic Network) observations, respectively, were derived and used in this study. The classifications of aerosol types were made on the basis of *Sp* value, vertical aerosol profiles, and columnar optical properties of aerosols. The *Sp* values for several major air pollutants that affect northern Taiwan were determined and further compared with previous studies, to identify the pollutant phase between *Sp* corresponding numerical relationships. The major issues studied in depth in the present study were (a) seasonal variation of optical properties and vertical distribution of aerosols; (b) single and two-layered structures of aerosols; (c) the typical *Sp* values associated with different aerosol types, particularly over northern Taiwan.

#### **2. Methodology**

#### *2.1. Site Description*

Aerosol observation data used in this current study were acquired at the National Central University (NCU), Taoyuan city (24.97◦N, 121.18◦E; 133 m above sea level, a.s.l.) in northern Taiwan. This site is located at the western edge of Taoyuan city, situated 50 km south of Taipei, the national capital of Taiwan, and serves as a rural site with no significant near-source emissions. The northern part of Taiwan belongs to subtropical climate zone. The site stays normally under the influence of south-westerly Asian monsoon in summer (June–August) and north-easterly monsoon in late autumn to winter (October–February). The weather over northern Taiwan is normally cloudy and humid during the summer, but relatively cloud-free and dry during the winter ([23]). Several studies have reported that northern Taiwan is positioned on the pathway of continental Asian outflow to the west Pacific during the pollution outbreaks ([54–58]).

#### *2.2. Ground-Based Remote Sensing Observations*

MPL is an effective instrument to provide both high vertical and temporal-resolution aerosol distribution, mainly based on the principle of elastic back-scattering of the emitted low-power high repeatability, eye-safe laser [59]. It consists of a solid crystal (ND: YLF, neodymium-doped yttrium lithium fluoride crystals) laser which emits radiation at 1064 nm wavelength and its second harmonic generation is at 527 nm wavelength (green light) with a pulse repetition rate of 2500 Hz. The receiver section consists of a telescope (Cassegrain-type) with a coaxial optical lens diameter of 20 cm, and the back scattered light detected by the telescope is made to fall on an avalanche photodiode (a semiconductor photomultiplier tube) to count the back scattered photons, and finally records the number of photons per second into the computer. Data are recorded in the format of a time resolution of 1 min, spatial resolution of 75 m. The NCU MPL system is a member of the NASA Micro-Pulse Lidar Network (MPLNET; http://mplnet.gsfc.nasa.gov; [60]) project. The instrument maintenance, calibration and data processing were following well to the MPLNET portal.

Direct-sun measurements of aerosol optical depth (AOD; τ) at different spectral wavelengths (440, 500, 675, 870, and 1020 nm), recorded by a Cimel sun–sky radiometer at EPA-NCU site, were obtained from AERONET (http://aeronet.gsfc.nasa.gov). A more in-depth description of AERONET data can be found elsewhere ([61]). In the present study, we used AERONET Level 2 and MPLNET beta level 2a data ([23]) for analysis and discussion. Measurements from co-located AERONET and MPLNET can provide accurate aerosol products such as the aerosol backscattering coefficient profile and lidar ratio (*Sp*) at 527 nm (hereafter all *Sp* calculated in this study refer to this wavelength). The *Sp* was obtained through the best agreement of AERONET Level 2 AOD and MPL vertically integrated AOD retrieved by Fernald's method ([62]). This parameter contains the information of aerosol type in

optical properties sense, which varied with the laser's wavelength, aerosol particle size distribution, and composition (i.e., physicochemical properties of refractive index) significantly ([63,64]). It is noted that the data used in this study only represent clear-sky conditions.

#### *2.3. Source Identification Through Back Trajectory Analysis*

In order to identify the source origin of aerosol transport, 5-day back trajectory calculations were carried out using the Hybrid Single-Particle Lagrangian Integrated Trajectory Version 4 (HYSPLIT; Draxler and Rolph, 2003) model. HYSPLIT is an air parcel trajectory and dispersion model maintained by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory and this model uses wind fields to trace air parcel transport through the atmosphere ([65,66]). In its backward mode, HYSPLIT integrates back in time the path of travel of an air parcel arriving at a receptor location defined by horizontal and vertical coordinates at a given time. Meteorological input data for the back-trajectory calculation were from the NCEP Global Data Assimilation System (GDAS) at 1◦ × 1◦ resolution.

#### **3. Results**

#### *3.1. Long-Term Data Analysis of Aerosol Optical Properties*

The long-term optical properties of aerosols at NCU station (EPA-NCU as the official name shown in AERONET and MPLNET) are shown in Figure 1. Figure 1a shows the monthly variations in AOD at 500 nm (τ500). The observed annual average τ500 was 0.41 ± 0.25 (range of 0.2–1). High values (greater than annual average) of τ500 were observed from February to May, and low values (less than or equal to annual average) during the remaining months. The standard deviation of τ500 was about 0.2 to 0.3, indicating a clue on the high variability of columnar aerosol loading. High τ500 values were found to be similar to the nearby AERONET station (i.e., Taipei\_CWB; 25◦N, 121◦E; 26 m a.s.l.; ≈25 km northeast of EPA-NCU site) in Taipei city, and low values of τ500 were similar to a high-altitude background AERONET site at mountain Lulin (23.51◦N, 120.92◦E; 2862 m a.s.l.; ≈100 km southeast of EPA-NCU) in central Taiwan. This indicates relatively higher aerosol loading over the study region and made it a complex aerosol environment. The monthly mean τ500 was the highest in March (0.72 ± 0.28), followed by April (0.58 ± 0.22). This was mainly due to the long-range transport of biomass-burning from Indochina region and dust aerosols from arid region in China [23]. It was further confirmed that the EPA-NCU stations have frequent long-range transport events, and to strengthen the transport mechanism, the vertical profiles of aerosol extinction coefficient and trajectories for origin of aerosols were analyzed.

The long-term analysis of the angstrom exponent between 440 nm and 870 nm (α440/870), which stands for aerosol particle sizes (higher value for smaller particle and vice versa), has been plotted in Figure 1b. Annual average of α440/870 was 1.26 ± 0.11 (ranged from 1.34 to 1.05). The maximum value ≈1.4 was found in February followed by the low in April (1.12 ± 0.32), and the minimum was found in September (1.05 ± 0.42). The smallest size of particle observed in February and March is due to the long-rang transported biomass-burning aerosols in the free atmosphere ([67]). Higher values of τ500 in April with lower values of α440/870 indicated the presence of dust aerosols due to Asian dust transport from the Gobi Desert.

Figure 1c shows the monthly *Sp* averaged from MPLNET data set during 2005–2012. The annual average *Sp* was 47 ± 21 sr. *Sp* was the highest in March (54 ± 23 sr), followed by 51 ± 23 sr in April, and the lowest value was observed during May (37 ± 17 sr). As aforementioned, the *Sp* is mainly dependent on aerosol physicochemical properties and can be further interpreted to aerosol types.

**Figure 1.** Monthly averaged (**a**) τ500, (**b**) α440/870, and (**c**) *Sp* at EPA-NCU site during 2005–2012. Vertical red bars indicate ±1 standard deviation from the mean.

#### *3.2. Monthly Aerosol Extinction Profiles*

Figure 2 shows the vertical profile of aerosol extinction coefficient for each month averaged for the period of 2005–2012. As is clearly seen in Figure 3, the extinction coefficient extended from the surface to ≈6 km during March, April, and May, whereas the extinction coefficient was almost zero at ≈4 km during other months. A small aerosol layer at 2–4 km was observed over the region during March. This type of aerosol layer is mainly attributed to the dry convicting lift of air pollutants from far-off regions and subsequent horizontal transport of aerosols ([23,68]). On average, the vertical aerosol distribution follows the scale height, higher aerosol extinction near the surface and decreases as height increases.

#### *3.3. Aerosol Vertical Distribution, Source Region, and Optical Properties*

According to the discussion in Section 3.2, the air masses in the PBL and free atmosphere could be from different source origins and imply different aerosol types. In order to clearly define the impacts of vertical distribution on the optical properties (i.e., τ500, α440/870, and *Sp*), we divided the classification of vertical distribution into two categories based on [23], i.e., single-layer (type 1) and two-layer (type 2) aerosol structures of the aerosol extinction coefficient.

**Figure 2.** Vertical profiles of monthly averaged aerosol extinction along with ±1 standard deviation during 2005–2012. The number of profiles for monthly average is denoted in the figure.

**Figure 3.** Averaged aerosol extinction profiles for (**a**) single-layer structure (type 1) and (**b**) two-layer structure (type 2). (**c**,**d**) indicate the number of days in the month corresponding to type 1 and type 2, respectively.

#### 3.3.1. Characteristics of Single-Layer Aerosol Structure

The averaged aerosol extinction along with one standard deviation for single aerosol layer structure (type 1) is shown in Figure 3a. The aerosols are concentrated in the lower troposphere within 2 km altitude and the averaged extinction coefficient value is less than 0.2 km−1 at the near surface. This confinement of aerosols was attributed to boundary layer dynamics and inversion layer below free atmosphere. The total number of days corresponding to type 1 was 55 days (total number of data points = 649) and there were a maximum of 15 days in October, followed by a total of 11 days in September (Figure 4c). The single-layered structure of aerosols mainly occurred in the autumn and winter, especially in September and October, as shown in Figure 3c.

**Figure 4.** (**a**) Five-day back trajectories for all cases of single-layer aerosol structure (type 1). (**b**) Aerosol source origins at EPA-NCU from (**c**) Asia continental (AC), (**d**) Pacific Ocean (PO), and (**e**) Southeast Asia (SA).

In order to explore the possible sources and types of aerosols, the backward trajectory simulations were employed. Five-day back trajectories (Figure 4a) made at a height of 500 m represent the occurrence height of maximum aerosol extinction. The back trajectories for type 1 showed disorder and originated from all the directions. We further divided the total area into three regions (Figure 4b), viz., the Asia continental region (AC; Mongolia, Tibet, Xinjiang, North China and Central China region, Japan, and South Korea), the Pacific Ocean region (PO; Northwestern Pacific), and the Southeast Asia region (SA; Southeast Asia and southern China). Back trajectory analysis showed that a total of 28 day (number of data points = 458), 12 day (number of data points = 59), and 21 day (number of data points = 154) air masses were coming from AC, PO, and SA regions to EPA-NCU site, respectively (Figure 4c,e). It is important to note that few trajectories passed through more than one region, which resulted in double counting of data numbers in Table 1.

**Table 1.** Aerosol optical properties for single-layer aerosol structure (type 1) with respect to different source regions. Data were averaged for 2005–2012 collected at the MPLNET EPA-NCU site. AERONET derived ω440, *g*440, *nr*, *ni* stand for single-scattering albedo at 440 nm wavelength, asymmetry factor at 440 nm wavelength, and real part and imaginary part of refractive index, respectively.

± denotes the one standard deviation.

Combining information of α440/870 and *Sp* can help us to distinguish the aerosol types. Figure 5a shows the scatter plot of α440/870 and *Sp* with code denoting τ500 together with the histograms representing spread of samples and color magnitude of for type 1. The τvalues were in the range of 0.1 to 0.6, with an average value of 0.21; α440/870 was between 0.6 and 1.8, with an average value of 1.26 ± 0.31. The *Sp* is concentrated between 30 and 60 sr with an average of 39 sr. Previous studies reported that the *Sp* values of (37 ± 9) sr and (33 ± 6) sr were for dust and marine aerosols, respectively [69].

**Figure 5.** Scatter plot and data spread distribution histograms of aerosol optical properties for (**a**) single-layer (type 1) and (**b**) two-layer (type 2) versions.

Table 1 presents the aerosol optical properties of type 1, and for the values in different source sectors. The τ500, α440/870, and *Sp* for AC region were 0.24 ± 0.11, 1.34 ± 0.19, and (39 ± 16) sr, respectively. The real part (*nr* = 1.45) of the refractive index was small and relatively close to the urban aerosol refractive index (*nr* = 1.46). The τ500, α440/870, and *Sp* for PO source region were 0.19 ± 0.1, 1.24 ± 0.31, and (30 ± 12) sr, respectively. The moderate high value of α440/870 suggests that the mixture of coarse mode sea salt aerosol and fine mode urban aerosol. The similar value of *Sp* about 33 ± 6 sr was also reported by [28] in the Indian Ocean. For the SA region, the average τ500 and α440/870 were 0.19 ± 0.08 and 1.32 ± 0.32, respectively, indicating the abundant fine mode particle aerosol compared to other source regions. The average of *Sp* was 38 ± 17 sr in the SA region; however, the data histogram (not shown) exhibited two distinguished peaks: the first peak ranged between 20 and 40 sr, whereas the second peak ranged between 45 and 60 sr. The median value of the first peak was about 30 sr corresponding to marine type aerosols, while the second peak was similar to the results from local pollutants. The complex of emission sources (i.e., marine, ship, local Taiwan island) in the SA region might need further study in the future when more data becomes available. Regarding to the AERONET derived single-scattering albedo at 440 nm wavelength (ω440) and asymmetry factor at 440 nm wavelength (*g*440), aerosols from AC and SA regions show similar values and suggest a moderate absorption compared to that from PO regions. The result implies the segment of source sector with backward trajectory may not be sufficient to aerosol classification for majority of data.

#### 3.3.2. Characteristics of Two-Layer Aerosol Structure

Two-layer structured vertical profiles have been classified as type 2 ([23]). In this type, we found another peak with an aerosol extinction coefficient at 2–4 km in addition to the extinction coefficient peak that appears at the near surface. Lower than 2 km, the aerosol vertical profile has a similar impact as type 1, but at the 2–4 km range, the average extinction coefficient is 0.25 km−1 in type 2, whereas it is only less than 0.1 km−1 in type 1. The two-layered structure mainly occurs in spring and winter seasons, and a few days in other months, as shown in Figure 3d.

The τ500, α440/870, and *Sp* for all observations (total number of data points = 503) under the spread and histograms for two-layer aerosol structure (type 2) are shown in Figure 5b. From the Figure 5b it can be observed that τ500 is between 0.1 and 1.5, with an average of 0.51 ± 0.26; α440/870 is between 0.34 and 1.85, with an average of 1.23 ± 0.26; and *Sp* ranges between 30 and 80 sr with an average of 52 ± 23 sr.

These two-layered aerosol structures high-up and low-down are caused by the different delivery mechanisms; therefore, they can be tracked by the use of air mass back trajectories analysis in the HYSPLIT model (e.g., [23]). In addition, according to a database (https://airtw.epa.gov.tw/CHT/Forecast/ Sand.aspx) provided by the Taiwan Environmental Protection Agency (EPA) which determines the date for a sandstorm event, we can classify dust transport as taking place down low. The dust and biomass-burning cases for two-layer aerosol structure are defined as follows:

#### *3.4. Dust Case*

From the vertical profile of the aerosol extinction coefficient, it can be seen that dust mainly transports in the height region of 500–2000 m (Figure 6a). We carefully checked the airflow source with backward trajectories; it was found that low in the atmosphere (500 m), the aerosol sources were from the northern desert regions of China. In addition, we also found that three dust events (on 18 March, 28 March, and 25 November in 2005) had influences on biomass-burning transport from Indochina through the backward trajectory analysis at 2500 m. A recent publication by [70] also demonstrated a long-range transport event with concurrent dust and biomass-burning aerosol layers based on EPA-NCU lidar observation. More study related to the coexisting of dust and biomass-burning over the west Pacific can be explored in the future.

**Figure 6.** Averaged aerosol extinction profiles for (**a**) type 2 dust source and (**b**) type 2 biomass-burning source. In the subplots, the respective air mass backward trajectories for dust (ended at the height of 500 m) and for biomass-burning (ended at the height of 3 km) cases are illustrated.

The τ500 for the dust events over the years was at about 0.45 ± 0.16 with the averaged α440/870 of about 1.10 ± 0.24. Coarse particle size was higher when compared to other regions, and *Sp* was found in the range of 20 to 60 sr with an average of 40 ± 16 sr (Figure 7a), and followed the normal Gaussian distribution with the median value of 40 sr. The estimation *Sp* of dust aerosols in African region was 37 ± 9 sr, and it also found that it changed in the course of dust transportation when mixed with urban pollutants ([71]).

**Figure 7.** Scatter plot and data spread distribution histograms of aerosol optical properties for (**a**) type 2 dust cases and (**b**) type 2 biomass-burning cases.

#### *3.5. Biomass-Burning Case*

The vertical distribution of aerosol extinction coefficient shows a significant secondary peak 0.1 km−1 in the height range of 3–4 km approximately (Figure 6b). Based on the back trajectory at the height of 3000 m above sea level, we found that the air mass sources were from the SA region (taken over 22 days; total number of data points = 313). The analyzed averaged optical characteristics for biomass-burning were τ500 of 0.58 ± 0.20, α440/870 of 1.22 ± 0.33, and *Sp* of 53 ± 21 sr (Table 2). The distribution of *Sp* shows double peaks, the first peak in the range of 20 to 50 sr and the second peak in the range of 60 to 70 sr (Figure 7b). Compared to the mean *Sp* value of 47 ± 21 sr in northern Taiwan, it was found that the first peak could be linked to mixture of anthropogenic and biomass-burning aerosols. When the air by the burning of biomass and other outside influences affects the area, it gives rise in *Sp* to a more apparent second peak (60 to 70 sr) due to high absorption of light by the high altitude aerosols. The similar value of *Sp* as 63 ± 10 sr for African savannah biomass-burning was also reported by [17].

**Table 2.** Same as Table 1 but for aerosol optical properties of a two-layered aerosol structure (type 2) with respect to different emission sources. Due to the layer being decoupled, the dust and biomass-burning layer can co-occur, which causes the double count issue of data number.

#### **4. Discussion**

In this section, we further discuss the *Sp* of urban, dust, and biomass-burning aerosols representing the subtropical region of East Asia based on aforementioned analysis with additional estimations. In the previous section, based on statistics of long-term (2005–2012) EPA-NCU MPL dataset, we found that the average value of *Sp* is 47 ± 21 sr. The results report a mixture of dust and biomass-burning aerosols. In order to quantify the *Sp* value of urban aerosols, we selected cases with two constraints: (1) type 1 vertical profile and only from AC source region, (2) no dust events. We estimated the mean *Sp* of urban aerosols to be 42 ± 18 sr, which is in agreement with the value of 45 ± 10 suggested by [19] (see Table 3). For oceanic aerosols, we suggested that the *Sp* value can be estimated from PO source region in type 1, which is 30 ± 12 sr. This value is very similar to the values (32–33) provided by [72]. It should be noted that a higher value observed in our study may have been due to the mixing of local urban aerosols during the transport to northern Taiwan.

**Table 3.** Comparison of *Sp* values (unit in sr) in the literature.

Regarding dust aerosol, we selected cases that met the criteria of type 1 classification and EPA dust event dates. Only one dust event was recorded on 29 January 2007, and comprises 37 profiles. As shown in Figure 8a, the dust plume was transported near the surface and constrained within 1.4 km height. The maximum aerosol extinction coefficient was 0.2 ± 0.04 km−1 observed at 1 km. Figure 8b shows the scattering plot of *Sp* and α440/870 for all type 1 data points, in which red dots highlight the dust signature. The α440/870 was about 1.05, suggesting a slightly coarser particle for transported dust when compared to the mean α440/870 value of 1.29 for type 1 at EPA-NCU station. The number distribution of *Sp* for dust data is shown in Figure 8c. The *Sp* ranged between 25 and 50 sr with an average of 34 ± 6 sr. The value shows good agreement with previous studies ([71,73]) that measured dust particles (Table 3). It is worth mentioning that a large portion of dust events may have a mixture of anthropogenic aerosols during the transport. Those mixed events can have a higher lidar ratio of up to 40 sr, as described in dust cases of type 2 profile.

The transport of biomass-burning aerosols to Taiwan is often characterized by upper-level transport, which makes it difficult to separate the biomass-burning *Sp* value from the total columnar mean *Sp* value (as obtained from the EPA-NCU MPLNET). The histogram of *Sp* number distribution for biomass-burning cases in type 2 shows a bimodal distribution (Figure 7b), where the first peak ranged from 30 to 50 sr and the second peak ranged from 60 to 80 sr. In type 2, the aerosol type contributing to the surface layer can be urban, oceanic, or dust, whereas biomass-burning aerosols are attributed to the upper level. Large portions of aerosols are suspended within the surface layer. As listed in Table 3, the mean *Sp* values for urban, oceanic, and dust are between 30–42 sr, and correspond to the first peak of Figure 7b. That suggests the domination of surface layer transport. In other words, the second peak of Figure 7b may represent the domination of upper-level transport, which is mainly attributed to biomass-burning aerosols. The mode *Sp* of the second peak was 69 ± 12 sr, representing biomass-burning aerosols originating from Indochina. Our result shows a little higher value compared to the literature value of 63 ± 10 sr for African biomass-burning ([17]).

**Figure 8.** Single-layer dust transfer cases: (**a**) aerosol extinction profiles, (**b**) scatter plots of *Sp* and α440/870 for type 1 (blue triangles) and dust (red diamonds) events, and (**c**) histogram of *Sp* number distribution.

#### **5. Conclusions**

Lidar ratio (*Sp*) is a distinctive aerosol optical property and can be used for aerosol type classification, which is important for pollution source identification and radiative forcing assessment. This study analysed the long-term (2005–2012) observations of MPLNET and AERONET from a rural site (EPA-NCU) in northern Taiwan located in the western North Pacific. Those observations, consisting of aerosol optical characteristics, extinction coefficient profiles, and lidar ratios, in conjunction with air mass trajectories, were further used to investigate the relationship between aerosol types and aerosol optical properties over the region. The important findings are as follows:

The findings of this study offer a reference for future attempts to monitor the sources of aerosol over Taiwan and western North Pacific. Results of *Sp* value provide useful references for aerosol classification and air pollution identification in this region. This kind of study is also important for resolving aerosol-perturbed atmospheric circulation (e.g., [74–76]) and temporal changes in regional air quality (e.g., [77,78]). Nevertheless, due to the principle of Mie elastic lidar, the *Sp* value only represents a columnar integrated number by using a retrieval method. The newly available lidar ratio datasets with data fusion technology, such as AERONET version 3 product (e.g., [34,79]), MPLNET version 3 depolarization ratio product, Raman lidar ([72]), and high spectral resolution lidar (e.g., [42–45]) do also give an aspect of aerosol classification. More observation and data analyses are needed to further explore the relationship between aerosol types and aerosol optical properties over the region.

**Author Contributions:** Conceptualization, S.-H.W.; methodology, H.-W.L., H.-Y.H., and Y.-C.W.; writing—original draft preparation, S.-H.W.; writing—review and editing, S.K.P.; project administration, N.-H.L. and E.J.W.; funding acquisition, S.-C.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded by the Taiwan Environmental Protection Administration under grant EPA-107- FA11-03-A071 and by the Ministry of Science and Technology under grant 108-2111-M-008-025.

**Acknowledgments:** The NASA MPLNET and AERONET are funded by the NASA Earth Observing System and Radiation Sciences Program. The authors gratefully acknowledge the NOAA Air Resources Laboratory for the provision of the HYSPLIT transport and dispersion model used in this publication. We are also grateful to the National Center for High-performance Computing for computer time and facilities.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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